Biomedical Signals Analysis and modeling

Objective: Some studies on schizophrenia showed an increased complexity in electroencephalography (EEG) whereas others detected a decreased complexity. Because this discrepancy might be due to the clinical features or complexity measures used, we employed two different complexity measures in a group of schizophrenics similar in illness duration (chronic) and symptom profile (residual). Methods: Right-handed chronic residual schizophrenic patients (10 male, 10 female) and age-and sex-matched 20 healthy controls were included in the study. Eyesclosed resting EEG series were measured through quantitative EEG band activities, the log energy entropy (LEE) values, and the Hurst exponents (HE) of EEG measurements were computed for each electrode site. Results: Significantly higher LEE values in the prefrontal, frontal, temporal and parietal locations were observed in schizophrenic patients compared with controls. HE values were significantly higher on the right frontal area in the schizophrenics. Patient group showed increased prefrontal, frontal and parietal delta activity, prefrontal, left temporal and right parietal theta activity and increased left temporal alpha activity. Discussion: In the present study, we found that chronic residual schizophrenia is associated with decreased complexity and increased smoothness in EEG. In addition, EEG of patients was characterized by obvious slowness at prefrontal and frontotemporal regions, dominantly. An integration of EEG complexity and frequency analysis can be proposed as an innovative tool in schizophrenia research.

Suicide, considered as one of the most leading causes of death, has not given enough and appropriate attention in order to reduce its rate such that the humans in all over the world deserve it. The problem addressed in this paper is analysis of the relation between an extra stimulus and physiological data's responses. In order to record the physiological data set from multiple subjects over many weeks, we used an acoustic startle during a Paced Auditory Serial Addition Task (PASAT) test that spontaneously leads subjects to real emotional reactivity, without any deliberate laboratory setting. Crucially, we show that, by inducing anxiety during the test, changes appear in Electrodermal activity, Electrocardiogram, Heart Rate and Respiration Rate. A wide range of physiological features from various analysis domains, including modeling, time/frequency analysis, an algorithm and etc., is proposed in order to find the best emotional reactivity feature to correlate them with emotional states which can be considered as a suicide factor. More specifically, this paper is focused on the EDA data analysis. Experimental results highlight that all cited techniques perform well and we achieved a high resolution of tonic and phasic components which allow us to measure the latency, onsets and amplitudes of EDA responses to a stimulus. This paper follows the association of recommendations for advancement of health care instruments.

The digital analysis of heart sounds has revealed itself as an evolving field of study. In recent years, numerous approaches to create decision support systems were attempted. This paper proposes two novel algorithms: one for the segmentation of heart sounds into heart cycles and another for detecting heart murmurs. The segmentation algorithm, based on the autocorrelation function to find the periodic components of the PCG signal had a sensitivity and positive predictive value of 89.2% and 98.6%, respectively. The murmur detection algorithm is based on features collected from different domains and was evaluated in two ways: a random division between train and test set and a division according to patients. The first returned sensitivity and specificity of 98.42% and 97.21% respectively for a minimum error of 2.19%. The second division had a far worse performance with a minimum error of 33.65%. The operating point was chosen at sensitivity 69.67% and a specificity 46.91% for a total e...

Heart sounds and murmurs have very small amplitude and frequency signals thus make it so difficult to hear without the correct tools. In clinical practice currently, physicians listen to the patient heart sound and murmurs by using the traditional technique as an example mechanical stethoscope which having low accuracy and could lead to the false diagnosis. Moreover, conventional method has no ability to record the sound measured. Worst still it is totally depending on the physician’s skills and experienced which this ability is decreased over time. This issue is highly important in early detection of heart sound abnormal. The stereo heart auscultation purposed in this research is to provide solutions rise from conventional technique. Furthermore, the sound signals produced from heart will be converted to the real-time graphically presented with time-frequency analysis, which provides more information about the heart conditions by sound produced. The system compromise hardware such ...

All living organism reflect their physiological and pathological process in term of signals called biomedical signals. These signals extract the useful information from biological system under investigation. Heart is an important part of body and any disorder can affect sounds produced by heart. Auscultation is one of the fundamental and non-invasive tools used for analysis of heart sounds. There are four types of heart sounds are produced due to mechanical activities of heart. Conventionally electrocardiogram is used for analysis of heart activates but due to defect in their signals and cannot detect low frequency signals. Phonocardiogram is provides the detection and analysis of low frequency heart sounds. In this paper, analysis is done on phonocardiographic signal which shows the significant effect as a function of age.

Since there are various difficulties associated with auscultation techniques (e.g., the detection/recognition of murmurs and sound tone changes within approximately one second), we have proposed an audiovisual based technique to examine and visualize heart sounds for both physicians and patients. To overcome auscultation difficulties, the technique can be used to assist in the understanding of the heartbeat, the detection of heart disease, and the digital database management of the auscultation examination. Auscultatory sounds are visualized by both an FFT image and a wavelet image to detect any abnormal heart sounds. A simple technique of pattern classification has been established using short-time Fourier transform and wavelet analyses to detect abnormal sounds. This new technique is expected to be simple and practical in this era of computer-managed clinical data. The result indicates that there is a possibility of developing a fully automatic detection system based on a map of standard sounds in the near future. A simple auscultation method is also expected to be developed for in-home use.

The goal of this research is to integrate Virtual Reality (VR) with the bilateral stimulation used in EMDR as a tool to relieve stress. We created a 15 minutes relaxation training program for adults in a virtual, relaxing environment in form of a walk in the woods. The target platform for the tool is HTC Vive, however it can be easily ported to other VR platforms. An integral part of this tool is a set of sensors, which serves as physiological measures to evaluate the effectiveness of such system. What is more, the system integrate visual (passing sphere), auditory (surround sound) and tactile signals (vibration of controllers). A pilot treatment programme, incorporating the above mentioned VR system, was carried out. Experimental group consisting of 28 healthy adult volunteers (office workers), participated in three different sessions of relaxation training. Before starting, baseline features such as subjectively perceived stress, mood, heart rate, galvanic skin response and muscle response were registered. The monitoring of physiological indicators is continued during the training session and one minute after its completion. Before and after the session, volunteers were asked to refill questionnaires regarding the current stress level and mood. The obtained results were analyzed in terms of variability over time: before, during and after the session.

In this paper the design of a Graphical User Interface (GUI) is proposed for healthcare applications. The GUI is designed in MATLAB environment and is specifically suitable for advanced phonocardiography (PCG). This diagnostic method is more and more important nowadays, due to the advances in signal processing methods and computer processors performances in order to detect with a simple PC based electronic stethoscope, pathologies otherwise detectable with much more expensive and time-consuming medical techniques and devices.

Background: In this paper, we developed a novel algorithm to detect the valvular split between the aortic and pulmonary components in the second heart sound which is a valuable medical information. Methods: The algorithm is based on the Reassigned smoothed pseudo Wigner-Ville distribution which is a modified time-frequency distribution of the Wigner-Ville distribution. A preprocessing amplitude recovery procedure is carried out on the analysed heart sound to improve the readability of the time-frequency representation. The simulated S2 heart sounds were generated by an overlapping frequency modulated chirp-based model at different valvular split durations. Results: Simulated and real heart sounds are processed to highlight the performance of the proposed approach. The algorithm is also validated on real heart sounds of the LGB-IRCM (Laboratoire de Génie biomédical-Institut de recherches cliniques de Montréal) cardiac valve database. The A2-P2 valvular split is accurately detected by processing the obtained RSPWVD representations for both simulated and real data.

Recent days Computer-aided design and image processing techniques are one of the most emerging useful tools for analysis of models in various medical, industrial and research areas. In medical field for the diagnosis of various diseases in vivo techniques including biochemical test, enzymatic tests are needed for continuous monitoring of patients. These diagnosis procedures are time consuming and inconvenient for patients. Therefore Computer assisted designing and image processing techniques are most promising non invasive tools for faster diagnosis and useful tool for designing of biomedical implants. In this paper portal vein was segmented from patient’s Liver computed tomography (CT) image by dedicated software. A Liver’s CT image of 38 year old Indian male patient’s was collected and by using imaged based software MIMICS (Materialise’s Interactive Medical Image Control System, Belgium) 3D model of HPV (hepatic portal vein) was obtained and Solid Work Professional (Dassault Syste...

A new method to detect heart sound that does not require machine learning is proposed. The heart sound is a time series event which is generated by the heart mechanical system. From the analysis of heart sound S-transform and the understanding of how heart works, it can be deducted that each heart sound component has unique properties in terms of timing, frequency, and amplitude. Based on these facts, a deterministic method can be designed to identify each heart sound components. The recorded heart sound then can be printed with each component correctly labeled. This greatly help the physician to diagnose the heart problem. The result shows that most known heart sounds were successfully detected. There are some murmur cases where the detection failed. This can be improved by adding more heuristics including setting some initial parameters such as noise threshold accurately, taking into account the recording equipment and also the environmental condition. It is expected that this method can be integrated into an electronic stethoscope biomedical system

The Least Mean Square (LMS) algorithm is a very important tool in the estimation and filtering of biomedical signals. Amongst these signals are the periodic and quasiperiodic. For example, the LMS algorithm was used to estimate the coefficients of the Fourier series at a given frequency or even in a spectral analysis. In this paper we study the behavior of the weights of the LMS algorithm when the signal to be estimated acts at very low frequencies. We prove theoretically that lower frequency noise affects the estimation of the weights at higher frequencies. We carried out simulations and showed that experimental findings are in agreement with the theoretical results. Moreover, we exemplify the problem with electrocardiogram signals (ECG).

In this paper we introduce a new approach to heart sounds biometric recognition based on Gram polynomials and probabilistic neural networks (PNN). The usage of heart sounds as physiological biometric traits was first introduced in [1], in which the authors proposed and started exploring this idea. Heart sound recognition is based on the analysis of PCG (PhonoCardioGram) sequences. The proposed system presents good performance obtaining an error rate of 13.70 % over a database of 50 people, containing multiple heart sequences per person, each lasting from 20 to 70 seconds.

Time frequency-based methods have been employed for event detection in audio signals. The spectrogram can be used to extract loudness and energy functions by taking the row sum. The loudness function has been differentiated to obtain the onset events of the audio. The same could be replicated in case of bio-signals such as phonocardiograms to extract onset and offset boundaries. Continuous Wavelet Transform based Time-Frequency algorithm for onset and offset Event-detection has been developed for Phonocardiograms and the same has been tested on the PCG dataset hosted in Physionet repository. To validate the simulation, real implementation has also been carried out with real heart sound recordings obtained from Bluetooth based stethoscope called the stemoscope. The simulation carried out using PCG sounds from the Physionet database gave an F1-score of 99.11%. Similar results were found using the real heart sound recordings from stemoscope with an F1-score of 99.30%. Experimental evaluation and Simulations both indicate that Continuous Wavelet Transform based time frequency algorithm can be used to derive onset and offset events of the sounds in the PCG signal.

A new method to detect heart sound that does not require machine learning is proposed. The heart sound is a time series event which is generated by the heart mechanical system. From the analysis of heart sound S-transform and the understanding of how heart works, it can be deducted that each heart sound component has unique properties in terms of timing, frequency, and amplitude. Based on these facts, a deterministic method can be designed to identify each heart sound components. The recorded heart sound then can be printed with each component correctly labeled. This greatly help the physician to diagnose the heart problem. The result shows that most known heart sounds were successfully detected. There are some murmur cases where the detection failed. This can be improved by adding more heuristics including setting some initial parameters such as noise threshold accurately, taking into account the recording equipment and also the environmental condition. It is expected that this method can be integrated into an electronic stethoscope biomedical system

This paper presents a study of the impact of clicks, and murmurs on cardiac sound S1, and S2, and the measure of severity degree through synchronization degree between frequencies, using bispectral analysis. The algorithm is applied on three groups of Phonocardiogram (PCG) signal: group A represents PCG signals having a morphology similar to that of the normal PCG signal without click or murmur, group B represents PCG signals with a click (reduced murmur), and group C represent PCG signals with murmurs. The proposed algorithm permits us to evaluate and quantify the relationship between the two sounds S1 and S2 on one hand and between the two sounds, click and murmur on the other hand. The obtained results show that the clicks and murmurs can affect both the heart sounds, and vice versa. This study shows that the heart works in perfect harmony and that the frequencies of sounds S1, S2, clicks, and murmurs are not accidentally generated; but they are generated by the same generator system. It might also suggest that one of the obtained frequencies causes the others. The proposed algorithm permits us also to determine the synchronization degree. It shows high values in group C; indicating high severity degrees, low values for group B, and zero in group A. The algorithm is compared to Short-Time Fourier Transform (STFT) and continuous wavelet transform (CWT) analysis. Although the STFT can provide correctly the time, it can't distinguish between the internal components of sounds S1 and S2, which are successfully determined by CWT, which, in turn, cannot find the relationship between them. The algorithm was also evaluated and compared to the energetic ratio. the obtained results show very satisfactory results and very good discrimination between the three groups. We can conclude that the three algorithms (STFT, CWT, and bispectral analysis) are complementary to facilitate a good approach and to better understand the cardiac sounds

In the last century, cardiovascular illnesses are the first death cause in developed countries. For this reason, many efforts have been made in order to develop sophisticated techniques for the early diagnoses of cardiac disorders. The Phonocardiogram (PCG) signals contain very useful information about the condition of the heart. By analyzing these signals, early detection and diagnosis of heart diseases can be done. It is also very useful in the case of infants, where ECG recording and other techniques are difficult to implement.

In the last century, cardiovascular illnesses are the first death cause in developed countries. For this reason, many efforts have been made in order to develop sophisticated techniques for the early diagnoses of cardiac disorders. The Phonocardiogram (PCG) signals contain very useful information about the condition of the heart. By analyzing these signals, early detection and diagnosis of heart diseases can be done. It is also very useful in the case of infants, where ECG recording and other techniques are difficult to implement. In this study, a classification method is proposed to classify normal and abnormal heart sound signals using random forests algorithm. The proposed framework was applied to a database of 100 heart sound signals which collected from the web site, firstly all the signals were processed using the wavelet technique to eliminate the noise from the signal, features were extracted from the enhanced signals and the most significant features was selected using the ...

A new method to detect heart sound that does not require machine learning is proposed. The heart sound is a time series event which is generated by the heart mechanical system. From the analysis of heart sound S-transform and the understanding of how heart works, it can be deducted that each heart sound component has unique properties in terms of timing, frequency, and amplitude. Based on these facts, a deterministic method can be designed to identify each heart sound components. The recorded heart sound then can be printed with each component correctly labeled. This greatly help the physician to diagnose the heart problem. The result shows that most known heart sounds were successfully detected. There are some murmur cases where the detection failed. This can be improved by adding more heuristics including setting some initial parameters such as noise threshold accurately, taking into account the recording equipment and also the environmental condition. It is expected that this method can be integrated into an electronic stethoscope biomedical system

One of the major challenges for prosthesis or Human-Robot Device development is to produce devices to perfectly mimic their natural counterparts. A very popular approach for prosthesis or Robot control is based on the use of Bio-signals. One of them is EMG (the electrical manifestation of the neuromuscular activation associated with a contracting muscle) collected from remnant or normal muscles and use them as control inputs for the artificial limb or Robot Device. As these devices, known as EMG-based Hands-Free ...

This paper is aimed at the identification of the boundaries of murmur present in heart sound. Heart murmurs provide crucial diagnosis information for several heart diseases such as natural or prosthetic valve dysfunction and heart failure. In order to find the valuable information about abnormal heart behavior, segmentation of the heart murmurs has to be performed. In this work we solve this problem using the wavelet decompositionsimplicity filter. In this algorithm, simplicity of the wavelet decomposed heart sound signal is measured and adaptively thresholded in order to discriminate S1/S2 sounds from murmurs. The method has been tested with stenosis and regurgitation (aortic, mitral, pulmonary, tricuspid) heart sounds and 89.10% sensitivity and 95.50 % specificity have been achieved.

Epilepsy is one of the most common neurological disorders that greatly impair patient' daily lives. Traditional epileptic diagnosis relies on tedious visual screening by neurologists from lengthy EEG recording that requires the presence of seizure (ictal) activities. Nowadays, there are many systems helping the neurologists to quickly find interesting segments of the lengthy signal by automatic seizure detection. However, we notice that it is very difficult, if not impossible, to obtain long-term EEG data with seizure activities for epilepsy patients in areas lack of medical resources and trained neurologists. Therefore, we propose to study automated epileptic diagnosis using interictal EEG data that is much easier to collect than ictal data. The authors are not aware of any report on automated EEG diagnostic system that can accurately distinguish patients' interictal EEG from the EEG of normal people. The research presented in this paper, therefore, aims to develop an automated diagnostic system that can use interictal EEG data to diagnose whether the person is epileptic. Such a system should also detect seizure activities for further investigation by doctors and potential patient monitoring. To develop such a system, we extract four classes of features from the EEG data and build a Probabilistic Neural Network (PNN) fed with these features. Leave-one-out cross-validation (LOO-CV) on a widely used epileptic-normal data set reflects an impressive 99.5% accuracy of our system on distinguishing normal people's EEG from patient's interictal EEG. We also find our system can be used in patient monitoring (seizure detection) and seizure focus localization, with 96.7% and 77.5% accuracy respectively on the data set.

Heart sounds and murmurs have very small amplitude and frequency signals thus make it so difficult to hear without the correct tools. In clinical practice currently, physicians listen to the patient heart sound and murmurs by using the traditional technique as an example mechanical stethoscope which having low accuracy and could lead to the false diagnosis. Moreover, conventional method has no ability to record the sound measured. Worst still it is totally depending on the physician’s skills and experienced which this ability is decreased over time. This issue is highly important in early detection of heart sound abnormal. The stereo heart auscultation purposed in this research is to provide solutions rise from conventional technique. Furthermore, the sound signals produced from heart will be converted to the real-time graphically presented with time-frequency analysis, which provides more information about the heart conditions by sound produced. The system compromise hardware such ...

Fetal cardiovascular monitoring is essential in assessing high-risk gestations during the third trimester. With available technologies and signal processing methods, several congenital heart diseases can be detected prenatally, and adequate treatment can be prepared in advance. Phonocardiography is one such method, with the advantage of being inexpensive and convenient compared to other commercially available solutions. In this paper, an easily adaptable process is introduced to classify fetal S 1 as regular or split heart sounds. The process also includes a simple heart sound segmentation, based on the Teager-Kaiser energy operator and a moving average filter. Multiple signal features were evaluated, including time domain, frequency domain, and time-frequency information. The feature-space was constructed by principal component analysis with different feature compositions and classified using k-means clustering. Classification accuracy was evaluated on a clinical recording where 1874 heart sounds had been detected. The recording was labelled by an expert with 892 regular and 762 split S 1 and 220 nondecidable or noisy segments, which served as the ground truth. The highest overall accuracy achieved with individual features was 90.7%. A possible improvement upon this design using multiple classification stages based on additional features is also described.

A new method for analyzing the time frequency dynamics of brain's background electrical activ ity is described. It is used to detect at least three main features of Parkinson's disease (PD) in its early stages: (1) hemispheric asymmetry in the time frequency characteristics (EEG) in the central recording areas of the motor cortex, (2) the emergence in these recording areas of EEG rhythms in the frequency range of 4-6 Hz and its relation to electromyograms (EMG) and the mechanical tremor of contralateral limbs in the case of tremor dominant PD, and (3) the disruption of the dominant rhythm corresponding to views generally held on the disorganization of different systems in PD.

Background and Objective: Electrocardiogram (ECG) is widely used for the detection and diagnosis of cardiac arrhythmias such as atrial fibrillation. Most of the computer-based automatic cardiac abnormality detection algorithms require accurate identification of ECG components such as QRS complexes in order to provide a reliable result. However, ECGs are often contaminated by noise and artifacts, especially if they are obtained using wearable sensors, therefore, identification of accurate QRS complexes often becomes challenging. Most of the existing denoising methods were validated using simulated noise added to a clean ECG signal and they did not consider authentically noisy ECG signals. Moreover, many of them are model-dependent and sampling-frequency dependent and require a large amount of computational time. Methods: This paper presents a novel ECG denoising technique using the variable frequency complex demodulation (VFCDM) algorithm, which considers noises from a variety of sources. We used the sub-band decomposition of the noise-contaminated ECG signals using VFCDM to remove the noise components so that better-quality ECGs could be reconstructed. An adaptive automated masking is proposed in order to preserve the QRS complexes while removing the unnecessary noise components. Finally, the ECG was reconstructed using a dynamic reconstruction rule based on automatic identification of the severity of the noise contamination. The ECG signal quality was further improved by removing baseline drift and smoothing via adaptive mean filtering. Results: Evaluation results on the standard MIT-BIH Arrhythmia database suggest that the proposed denoising technique provides superior denoising performance compared to studies in the literature. Moreover, the proposed method was validated using real-life noise sources collected from the noise stress test database (NSTDB) and data from an armband ECG device which contains significant muscle artifacts. Results from both the wearable armband ECG data and NSTDB data suggest that the proposed denoising method provides significantly better performance in terms of accurate QRS complex detection and signal to noise ratio (SNR) improvement when compared to some of the recent existing denoising algorithms. Conclusions: The detailed qualitative and quantitative analysis demonstrated that the proposed denoising method has been robust in filtering varieties of noises present in the ECG. The QRS detection performance of the denoised armband ECG signals indicates that the proposed denoising method has the potential to increase the amount of usable armband ECG data, thus, the armband device with the proposed denoising method could be used for long term monitoring of atrial fibrillation.

Diagnosis of Parkinson's disease (PD) in the early stages is very critical for effective treatments. In this paper, we propose a simple and low-cost biomarker to diagnose PD, using the electroencephalography (EEG) signals. In the proposed method, EEG is used to detect the brain electrical activities in internal regions of brain, e.g., basal ganglia (BG). Based on the high correlation between PD and brain activities in the BG, the proposed method provides a highly accurate PD diagnostic measure. Moreover, we obtain a quantitative measure of the disease severity, using the spectral analysis of extracted brain sources. The proposed method is denoted by Parkinson's disease stage detection (PDSD). The PDSD includes brain sources separation and localization steps. The accuracy of the method in detection and quantification of PD is evaluated and verified by using information of ten patients and ten healthy people. The results show that there is a significant difference in the number of brain sources within the BG region, as well as their power spectral density, between healthy cases and patients. The accuracy and the cross-validation error of PDSD to detect PD are 95% and 6.25%, respectively. Furthermore, it is shown that the total power of extracted brain sources within the BG region in the α and β rhythms can be used effectively to determine the severity of PD.

The facial and physiological sensor-based emotion recognition methods are two
popular methods of emotion recognition. The proposed research is the first of its kind
in real-time emotion recognition that combines skin conductance signals with the
visual-based facial emotion recognition (FER) method on a Raspberry Pi. This research
includes stepwise documentation of method for automatic real-time face detection
and FER on portable hardware. Further, the proposed work comprises experimentation
related to video induction and habituation methods with FER and the galvanic skin
response (GSR) method. The GSR data are recorded as skin conductance and represent
the subject’s behavioral changes in the form of emotional arousal and face emotion
recognition on the portable device. The article provides a stepwise implementation of
the following methods: (a) the skin conductance representation from the GSR sensor
for arousal; (b) gathering visual inputs for identifying the human face; (c) FER from the
camera module; and (d) experimentation on the proposed framework. The key feature
of this article is the comprehensive documentation of stepwise implementation and
experimentation, including video induction and habituation experimentation. An
illuminating aspect of the proposed method is the survey of GSR trademarks and the
conduct of psychological experiments. This study is useful for emotional computing
systems and potential applications like lie detectors and human–machine interfaces,
devices for gathering user experience input, identifying intruders, and providing portable
and scalable devices for experimentation. We termed our approaches "sensovisual"
(sensors + visual) and "Emosense" (emotion sensing).
Keywords: Galvanic skin response, Raspberry pi, Face detection, Machine learning,
Emotion recognition, Facial emotion recognition, Affective computing, Computer
vision, Psychology, Haar cascade, Human–machine interface (HMI), Data fusion,
Physiological signal, Habitation test, Video induction, Stimuli, Correlation matrix,
Physiological signal-based emotion recognition, Sensovisual, Emosense

Diseases of the cardiovascular system are one of the major causes of death worldwide. These diseases could be quickly detected by changes in the sound created by the action of the heart. This dynamic auscultations need extensive professional knowledge and emphasis on listening skills. There is also an unmet requirement for a compact cardiac condition early warning device. In this paper, we propose a prototype of a digital stethoscopic system for the diagnosis of cardiac abnormalities in real time using machine learning methods. This system consists of three subsystems that interact with each other (1) a portable digital subsystem of an electronic stethoscope, (2) a decision-making subsystem, and (3) a subsystem for displaying and visualizing the results in an understandable form. The electronic stethoscope captures the patient's phonocardiographic sounds, filters and digitizes them, and then sends the resulting phonocardiographic sounds to the decision-making system. The decision-making system classifies sounds into normal and abnormal using machine learning techniques, and as a result identifies abnormal heart sounds. The display and visualization subsystem demonstrates the results obtained in an understandable way not only for medical staff, but also for patients and recommends further actions to patients. As a result of the study, we obtained an electronic stethoscope that can diagnose cardiac abnormalities with an accuracy of more than 90%. More accurately, the proposed stethoscope can identify normal heart sounds with 93.5% accuracy, abnormal heart sounds with 93.25% accuracy. Moreover, speed is the key benefit of the proposed stethoscope as 15 s is adequate for examination.

The purpose of computer-aided diagnosis (CAD) systems is to improve the detection of diseases in a shorter time and with reduced subjectivity. A robust system frequently requires a noise-free input signal. For CADs which use heart sounds, this problem is critical as heart sounds are often low amplitude and affected by some unavoidable sources of noise such as movement artifacts and physiological sounds. Removing noises by using denoising algorithms can be beneficial in improving the diagnostics accuracy of CADs. In this study, four denoising algorithms were investigated. Each algorithm has been carefully adapted to fit the requirements of the phonocardiograph signal. The effect of the denoising algorithms was objectively compared based on the improvement it introduces in the classification performance of the heart sound dataset. According to the findings, using denoising methods directly before classification decreased the algorithm's classification performance because a murmur was also treated as noise and suppressed by the denoising process. However, when denoising using Wiener estimation-based spectral subtraction was used as a preprocessing step to improve the segmentation algorithm, it increased the system's classification performance with a sensitivity of 96.0%, a specificity of 74.0%, and an overall score of 85.0%. As a result, to improve performance, denoising can be added as a preprocessing step into heart sound classifiers that are based on heart sound segmentation.

A major challenge in radiation oncology is the prediction and optimization of clinical responses in a personalized manner. Recently, nanotechnology-based cancer treatments are being combined with photodynamic therapy (PDT) and photothermal therapy (PTT). Predictive models based on machine learning techniques can be used to optimize the clinical setup configuration, including such parameters as laser radiation intensity, treatment duration, and nanoparticle features. In this article we demonstrate a methodology that can be used to identify the optimal treatment parameters for PDT and PTT by collecting data from in vitro cytotoxicity assay of PDT/PTT-induced cell death using a single nanocomplex. We construct three machine learning prediction models, employing regression, interpolation, and low-degree analytical function fitting, to predict the laser radiation intensity and duration settings that maximize the treatment efficiency. To examine the accuracy of these prediction models, we construct a dedicated dataset for PDT, PTT, and a combined treatment; this dataset is based on cell death measurements after light radiation treatment and is divided into training and test sets. The preliminary results show that the performance of all three models is sufficient, with death rate errors of 0.09, 0.15, and 0.12 for the regression, interpolation, and analytical function fitting approaches, respectively. Nevertheless, due to its simple form, the analytical function method has an advantage in clinical application and can be used for further analysis of the sensitivity of performance to the treatment parameters. Overall, the results of this study form a baseline for a future personalized prediction model based on machine learning in the domain of combined nanotechnology-and phototherapy-based cancer treatment.

This paper details a new method for heart sounds segmentation based on Teager energy operator which, to the best of our knowledge, is used for the first time in this work to perform heart sound segmentation. The proposed segmentation method divides the signal into periods showing the two dominant heart sounds S 1 and S 2 , and time durations of the systole and diastole. The method is implemented in the MATLAB environment using 30 normal subjects and a sample of various abnormal conditions drawn from the internet. The proposed method simplifies the presentation of heart sound signals obtained with a digital stethoscope. The resulting signals are more meaningful and easier to judge even for an unskilled doctor, thus eliminating the need for an expert physician. The main advantages of this method are employability in noisy environment, reduction of the time needed for examination process, reliability, and ease of use. The proposed method gives a sensitivity level of 99.1%, positive predictivity of 97.7%, specificity of 97.43%, and 98.3% accuracy.

Heart sounds and murmurs have very small amplitude and frequency signals thus make it so difficult to hear without the correct tools. In clinical practice currently, physicians listen to the patient heart sound and murmurs by using the traditional technique as an example mechanical stethoscope which having low accuracy and could lead to the false diagnosis. Moreover, conventional method has no ability to record the sound measured. Worst still it is totally depending on the physician’s skills and experienced which this ability is decreased over time. This issue is highly important in early detection of heart sound abnormal. The stereo heart auscultation purposed in this research is to provide solutions rise from conventional technique. Furthermore, the sound signals produced from heart will be converted to the real-time graphically presented with time-frequency analysis, which provides more information about the heart conditions by sound produced. The system compromise hardware such ...

In this paper, a time-frequency spectral analysis software (Heart Sound Analyzer) for the computer-aided analysis of cardiac sounds has been developed with Lab-VIEW. Software modules reveal important information for cardiovascular disorders, it can also assist to general physicians to come up with more accurate and reliable diagnosis at early stages. Heart sound analyzer (HSA) software can overcome the deficiency of expert doctors and help them in rural as well as urban clinics and hospitals. HSA has two main blocks: data acquisition and preprocessing, time-frequency spectral analyses. The heart sounds are first acquired using a modified stethoscope which has an electret microphone in it. Then, the signals are analysed using the time-frequency/scale spectral analysis techniques such as STFT, Wigner-Ville distribution and wavelet transforms. HSA modules have been tested with real heart sounds from 35 volunteers and proved to be quite efficient and robust while dealing with a large variety of pathological conditions.

This paper presents a new algorithm to translate electromyogram (EMG) signals, produced during facial muscle contractions, into computer cursor actions. These cursor actions are: left, right, up, down and left-click. The translation is performed in real-time and the classification of the EMG signals is based on features derived from the spectral analysis of the electromyograms. We have sought to improve upon the original EMG-based cursor control system described in [1] [2], and have devised a new system setup and algorithm for the translation of the EMG signals into cursor actions. The two systems were compared using Matlab simulations and point-and-click trials. The simulation results show a marked improvement of the new system over the previous system in terms of classification accuracy, and the preliminary point-and-click tests indicate the possibility of a faster operation for the new system when compared to the old system.

This paper describes the development and evaluation of a system that processes electromyogram (EMG) signals voluntarily produced by a user and collected through electrodes placed in the user's forehead and temples, for the purpose of hands-free manipulation of the computer cursor. The goal of the system is to enable the user to step the cursor in two directions: horizontally (left and right) and vertically (up and down). In addition, the system allows the user to execute a "click" operation. The immediate area of application and main motivation for the development of this system is in the area of assistive technologies, to facilitate the access of individuals with motor disabilities to computers. However, the system can also be seen as a hands-free remote manipulator control in two axes.

FPGAs provide an ideal template for run-time reconfigurable (RTR) designs. Only recently have RTR enabling design tools that bypass the traditional synthesis and bit stream generation process for FPGAs become available. Heart auscultation which is the interpretation of sounds produced by the heart is a fundamental tool in the diagnosis of heart disease. It is the most commonly used technique for screening and diagnosis in primary health care. This study aims at utilizing the discrete wavelet packet transforms in early detection of an Aortic Stenosis (AS) using heart sound data collected at Sussex University Hospital in England. From the data analysis, a criteria has been proposed for the detection of the AS disease from the heart sound data.

Photodynamic therapy (PDT) and photothermal therapy (PTT) are promising therapeutic methods for cancer treatment; however, as single modality therapies, either PDT or PTT are still limited in their success rate. A dual application of both PDT and PTT, in a combined protocol, has gained immense interest. In this study, gold nanoparticles (AuNPs) are conjugated with a PDT agent, meso-tetrahydroxyphenylchlorin (mTHPC) photosensitizer, designed as nanotherapeutic agents that can activate a dual photodynamic/photothermal therapy. The AuNP-mTHPC complex is biocompatible, soluable, and photostable. PDT efficiency is high because of immediate reactive oxygen species (ROS) production upon mTHPC activation by the 650 nm laser which decreased mitochondrial membrane potential (∆ψm). Likewise, the AuNP-mTHPC complex is used as a photoabsorbing (PTA) agent for PTT, due to efficient plasmon absorption and excellent photothermal conversion characteristics of AuNPs under laser irradiation at 532 nm....

Photodynamic therapy (PDT) and photothermal therapy (PTT) are promising therapeutic methods for cancer treatment; however, as single modality therapies, either PDT or PTT is still limited in its success rate. A dual application of both PDT and PTT, in a combined protocol, has gained immense interest. In this study, gold nanoparticles (AuNPs) were conjugated with a PDT agent, meso-tetrahydroxyphenylchlorin (mTHPC) photosensitizer, designed as nanotherapeutic agents that can activate a dual photodynamic/photothermal therapy in SH-SY5Y human neuroblastoma cells. The AuNP-mTHPC complex is biocompatible, soluble, and photostable. PDT efficiency is high because of immediate reactive oxygen species (ROS) production upon mTHPC activation by the 650-nm laser, which decreased mitochondrial membrane potential (∆ ψm). Likewise, the AuNP-mTHPC complex is used as a photoabsorbing (PTA) agent for PTT, due to efficient plasmon absorption and excellent photothermal conversion characteristics of AuNPs under laser irradiation at 532 nm. Under the laser irradiation of a PDT/PTT combination, a twofold phototoxicity outcome follows, compared to PDT-only or PTT-only treatment. This indicates that PDT and PTT have synergistic effects together as a combined therapeutic method. Our study aimed at applying the AuNP-mTHPC approach as a potential treatment of cancer in the biomedical field.

This research examines the use of fSEMG (facial Surface Electromyogram) to recognise speech commands in English and German language without evaluating any voice signals. The system is designed for applications based on speech commands for Human Computer Interaction (HCI). An effective technique is presented, which uses the facial muscle activity of the articulatory muscles and human factors for silent vowel recognition. The difference in the speed and style of speaking varies between experiments, and this variation appears to be more pronounced when people are speaking a different language other than their native language. This investigation reports measuring the relative activity of the articulatory muscles for recognition of silent vowels of German (native) and English (foreign) languages. In this analysis, three English vowels and three German vowels were used as recognition variables. The moving root mean square (RMS) of surface electromyogram (SEMG) of four facial muscles is used to segment the signal and to identify the start and end of a silently spoken utterance. The relative muscle activity is computed by integrating and normalising the RMS values of the signals between the detected start and end markers. The output vector of this is classified using a back propagation neural network to identify the voiceless speech. The cross-validation was performed to test the reliability of the classification. The data is also tested using K-means clustering technique to determine the linearity of separation of the data. The experimental results show that this technique yields high recognition rate when used for all participants in both languages. The results also show that the system is easy to train for a new user and suggest that such a system works reliably for simple vowel based commands for human computer interface when it is trained for a user, who can speak one or more languages and for people who have speech disability.

Heart sound is a kind of bio-sound, mainly through the media pass sound signals. The measures of the heart sound signals involved in acoustics, fluid mechanics research. when added some noise for selecting pure PCG signal by using adaptive white Gaussian noise. Than denoise the PCG signal by using Discrete Wavelet transform and decomposes a signal into a 4 level of basic functions. These basic functions are called Discrete wavelet transform (DWT), which transforms a discrete time signal to a discrete wavelet representation. Experiments are conducted on 23 different recordings of heart sound where experiment working on 2 normal signal and 19 Abnormal signals where working on the accuracy of the PCG signal that is depending on the training and testing data. Segmentation process based on Shannon entropy method for low amplitude and GSF is based on high amplitude. Toward this objective, after preprocessing the PCG signal, for feature extraction of the PCG signal fixed windows were moved on the preprocessed signal, and in each analysis window, two frequency-and amplitude-based features were calculated from the excerpted segment. In order to recognize the delineated PCG sounds, first, S1 and S2 were detected. Then, a new DS was regenerated from the signal whose S1 and S2 were eliminated to detect occasional S3 and S4 sounds. Finally, probable murmurs and souffles were spotted. The proposed algorithm was applied to 6 beat PCG. signals gathered from patients with different valve diseases. This feature Extraction of the PCG signal is use PCA for reducing 216 dimensions to 7 dimensions of the PCG signal. and the classifier is used SVM method which is found the normal and abnormal heart sound of the PCG signal.

Accurate localization of heart beats in phonocardiogram (PCG) signal is very crucial for correct segmentation and classification of heart sounds into S1 and S2. This task becomes challenging due to inclusion of noise in acquisition process owing to number of different factors. In this paper we propose a system for heart sound localization and classification into S1 and S2. The proposed system introduces the concept of quality assessment before localization, feature extraction and classification of heart sounds. The signal quality is assessed by predefined criteria based upon number of peaks and zero crossing of PCG signal. Once quality assessment is performed, then heart beats within PCG signal are localized, which is done by envelope extraction using homomorphic envelogram and finding prominent peaks. In order to classify localized peaks into S1 and S2, temporal and time-frequency based statistical features have been used. Support Vector Machine using radial basis function kernel i...

A new method to detect heart sound that does not require machine learning is proposed. The heart sound is a time series event which is generated by the heart mechanical system. From the analysis of heart sound S-transform and the understanding of how heart works, it can be deducted that each heart sound component has unique properties in terms of timing, frequency, and amplitude. Based on these facts, a deterministic method can be designed to identify each heart sound components. The recorded heart sound then can be printed with each component correctly labeled. This greatly help the physician to diagnose the heart problem. The result shows that most known heart sounds were successfully detected. There are some murmur cases where the detection failed. This can be improved by adding more heuristics including setting some initial parameters such as noise threshold accurately, taking into account the recording equipment and also the environmental condition. It is expected that this method can be integrated into an electronic stethoscope biomedical system

In this paper, a time-frequency spectral analysis software (Heart Sound Analyzer) for the computer-aided analysis of cardiac sounds has been developed with Lab-VIEW. Software modules reveal important information for cardiovascular disorders, it can also assist to general physicians to come up with more accurate and reliable diagnosis at early stages. Heart sound analyzer (HSA) software can overcome the deficiency of expert doctors and help them in rural as well as urban clinics and hospitals. HSA has two main blocks: data acquisition and preprocessing, time-frequency spectral analyses. The heart sounds are first acquired using a modified stethoscope which has an electret microphone in it. Then, the signals are analysed using the time-frequency/scale spectral analysis techniques such as STFT, Wigner-Ville distribution and wavelet transforms. HSA modules have been tested with real heart sounds from 35 volunteers and proved to be quite efficient and robust while dealing with a large variety of pathological conditions.

The goal of this research is to integrate Virtual Reality (VR) with the bilateral stimulation used in EMDR as a tool to relieve stress. We created a 15 minutes relaxation training program for adults in a virtual, relaxing environment in form of a walk in the woods. The target platform for the tool is HTC Vive, however it can be easily ported to other VR platforms. An integral part of this tool is a set of sensors, which serves as physiological measures to evaluate the effectiveness of such system. What is more, the system integrate visual (passing sphere), auditory (surround sound) and tactile signals (vibration of controllers). A pilot treatment programme, incorporating the above mentioned VR system, was carried out. Experimental group consisting of 28 healthy adult volunteers (office workers), participated in three different sessions of relaxation training. Before starting, baseline features such as subjectively perceived stress, mood, heart rate, galvanic skin response and muscle response were registered. The monitoring of physiological indicators is continued during the training session and one minute after its completion. Before and after the session, volunteers were asked to refill questionnaires regarding the current stress level and mood. The obtained results were analyzed in terms of variability over time: before, during and after the session.

Skin diseases impair the quality of life of those affected and, in the case of skin cancer, can even lead to death. Early detection allows the initiation of appropriate therapy with the aim of alleviating or even completely curing the disease. Such early detection at the subcellular level can be realized without the use of exogenous markers and completely non-invasively using multiphoton tomography1.
Interpretation of multiphoton data is performed by medical professionals with many years of experience. Depending on the extent of the tomograph's depth scans, extensive data must be viewed, evaluated, and documented. At this point, we provide an automated evaluation to support the medical personnel. In addition, our novel evaluation option is intended to strengthen the acceptance and prevalence of multiphoton tomography in the clinical environment.

Epileptic seizure is a sudden alteration of behavior owing to a temporary change in the electrical functioning of the brain. There is an urgent demand for an automatic epilepsy detection system using electroencephalography (EEG) for clinical application. In this paper, the EEG signal is divided into short time frames. Discrete wavelet transform is used to decompose each frame into a number of subbands. Different entropies as well as a group of features with which to characterize the spike events are extracted from each subband signal of an EEG frame. The features extracted from individual subbands are concatenated, yielding a high-dimensional feature vector. A discriminative subset of features is selected from the feature vector using a graph eigen decomposition (GED)-based approach. Thus, the reduced number of features obtained is effective for differentiating the underlying characteristics of EEG signals that indicate seizure events and those that indicate nonseizure events. The G...