DIGITAL ANALYSIS OF CARDIAC ACOUSTIC SIGNALS IN CHILDREN

DIGITAL ANALYSIS OF CARDIAC ACOUSTIC SIGNALS IN CHILDREN Milad El-Segaier, MD Division of Paediatric Cardiology, Department of Paediatrics, Lund University Hospital, Lund, Sweden SUMMARY Despite tremendous development in cardiac imaging, use of the stethoscope and cardiac auscultation remains the primary diagnostic tool in evaluation of cardiac pathology. With the advent of miniaturized and powerful technology for data acquisition, display and digital signal processing, the possibilities for detecting cardiac pathology by signal analysis have increased. The objective of this study was to develop a simple, cost-effective diagnostic tool for analysis of cardiac acoustic signals. Heart sounds and murmurs were recorded in 360 children with a single-channel device and in 15 children with a multiple-channel device. Time intervals between acoustic signals were measured. Short-time Fourier transform (STFT) analysis was used to present the acoustic signals to a digital algorithm for detection of heart sounds, define systole and diastole and analyse the spectrum of a cardiac murmur. A statistical model for distinguishing physiological murmurs from pathological findings was developed using logistic regression analysis. The receiver operating characteristic (ROC) curve was used to evaluate the discriminating ability of the developed model. The sensitivities and specificities of the model were calculated at different cut-off points. Signal deconvolution using blind source separation (BSS) analysis was performed for separation of signals from different sources. The first and second heart sounds (S1 and S2) were detected with high accuracy (100% for the S1 and 97% for the S2) independently of heart rates and presence of a murmur. The systole and diastole were defined, but only systolic murmur was analysed in this work. The developed statistical model showed excellent prediction ability (area under the curve, AUC = 0.995) in distinguishing a physiological murmur from a pathological one with high sensitivity and specificity (98%). In further analyses deconvolution of the signals was successfully performed using blind separation analysis. This yielded two spatially independent sources, heart sounds (S1 and S2) in one component, and a murmur in another. The study supports the view that a cost-effective diagnostic device would be useful in primary health care. It would diminish the need for referring children with cardiac murmur to cardiac specialists and the load on the health care system. Likewise, it would help to minimize the psychological stress experienced by the children and their parents at an early stage of the medical care

Novel neural approaches to data topology analysis and telemedicine

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen676. INGEGNERIA ELETTRICAnoopenRandazzo, Vincenz

PROCESSING AND CLASSIFICATION OF PHYSIOLOGICAL SIGNALS USING WAVELET TRANSFORM AND MACHINE LEARNING ALGORITHMS

Over the last century, physiological signals have been broadly analyzed and processed not only to assess the function of the human physiology, but also to better diagnose illnesses or injuries and provide treatment options for patients. In particular, Electrocardiogram (ECG), blood pressure (BP) and impedance are among the most important biomedical signals processed and analyzed. The majority of studies that utilize these signals attempt to diagnose important irregularities such as arrhythmia or blood loss by processing one of these signals. However, the relationship between them is not yet fully studied using computational methods. Therefore, a system that extract and combine features from all physiological signals representative of states such as arrhythmia and loss of blood volume to predict the presence and the severity of such complications is of paramount importance for care givers. This will not only enhance diagnostic methods, but also enable physicians to make more accurate decisions; thereby the overall quality of care provided to patients will improve significantly. In the first part of the dissertation, analysis and processing of ECG signal to detect the most important waves i.e. P, QRS, and T, are described. A wavelet-based method is implemented to facilitate and enhance the detection process. The method not only provides high detection accuracy, but also efficient in regards to memory and execution time. In addition, the method is robust against noise and baseline drift, as supported by the results. The second part outlines a method that extract features from ECG signal in order to classify and predict the severity of arrhythmia. Arrhythmia can be life-threatening or benign. Several methods exist to detect abnormal heartbeats. However, a clear criterion to identify whether the detected arrhythmia is malignant or benign still an open problem. The method discussed in this dissertation will address a novel solution to this important issue. In the third part, a classification model that predicts the severity of loss of blood volume by incorporating multiple physiological signals is elaborated. The features are extracted in time and frequency domains after transforming the signals with Wavelet Transformation (WT). The results support the desirable reliability and accuracy of the system

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

On the Recognition of Emotion from Physiological Data

This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure

Remote measurements of heart valve sounds for health assessment and biometric identification

Heart failure will contribute to the death of one in three people who read this thesis; and one in three of those who don't. Although in order to diagnose patients’ heart condition cardiologists have access to electrocardiograms, chest X-rays, ultrasound imaging, MRI, Doppler techniques, angiography, and transesophageal echocardiography, these diagnostic techniques require a cardiologist’s visit, are expensive, the examination time is long and so are the waiting lists. Furthermore abnormal events might be sporadic and thus constant monitoring would be needed to avoid fatalities. Therefore in this thesis we propose a cost effective device which can constantly monitor the heart condition based on the principles of phonocardiography, which is a cost-effective method which records heart sounds. Manual auscultation is not widely used to diagnose because it requires considerable training, it relies on the hearing abilities of the clinician and specificity and sensitivity for manual auscultation are low since results are qualitative and not reproducible. However we propose a cheap laser-based device which is contactless and can constantly monitor patients’ heart sounds with a better SNR than the digital stethoscope. We also propose a Machine Learning (ML) aided software trained on data acquired with our device which can classify healthy from unhealthy heart sounds and can perform biometric authentication. This device might allow development of gadgets for remote monitoring of cardiovascular health in different settings

Kablosuz Vücut Algılayıcı Ağları Ve Uzaktan Hasta Takip Sistemi

TÜBİTAK EEEAG Proje01.07.2017Bu projede, kalp yetmezligi, yüksek tansiyon, diyabet ve kronik obstrüktif akciger hastalıgı(KOAH ) gibi kronik hastalıklara sahip hastaların uzaktan izlenmesi için günümüz bilgi vemobil iletisim teknolojilerini kullanan bir teletıp sisteminin gerçeklestirilmesi amaçlanmıstır.Çalısmanın baslangıcını 2010 yılında dünya saglık örgütü (WHO) nun tele-tıp alanındagereksinim duydugu arastırmalar ve aynı yıllarda Eric TOPOL un yaptıgı bir dizi konferans vekitap yayınları olusturmustur. Ancak son yıllarda bu alanda yapılan yayınlar, konferanslar veendüstriyel girisimler öyle artmıstır ki; yaptıgımız çalısmanın öneminden çok endüstriyelyeterliligimizin ve akademik girisimlerimizin gölgelenmemesi endisesi ile projetamamlanmıstır.Proje içeriginde Bluetooth ve Zigbee gibi alternatif teknolojilerde karsılasılan güç tüketimi vegirisim problemlerinin üstesinden gelebilecek IEEE 802.15.6 radyosu yazılım ve donanımıylabirlikte gerçeklestirilmistir. Söz konusu standart 2.36 ile 2.4 GHz arasında 600 kHz likkanallarda sadece saglık verilerinin aktarılabilecegi haberlesme kanallarını öngörmektedir.IEEE 802.15.6 standardının öngördügü haberlesmeyi gerçeklestirecek radyo ve uygulamalarıdestekleyecek yazılımın mevcut olmamasından dolayı projenin önemli bir is yükünü buçalısmalar olusturmustur. Projeyle birlikte uygulamaların gerektirdigi cihazlar arası otomatikhaberlesme, ag kurulumu ve servis tanıma gibi makinadan makinaya haberlesme protokolüde gerçeklestirilmistir. Projenin hedefledigi kablo esdegeri güvenilirlik ve düsük güçlü radyoihtiyacı önemli ölçüde karsılanmıstır.Projenin diger iki temel bileseni insan vücudundan saglık verisini toplayacak algılayıcılar vehasta ile saglık personeli arasında iletisimi saglayıp, saglık personeline yardımcı olacak karardestek sistemi yazılımıdır. Algılayıcılar olarak EKG, Solunum, SPO2, tansiyon, vücut ısısı,agırlık ve ivme ölçüm sensörleri gelistirilmistir. Karar destek yazılımı iki ana bölümdenolusmaktadır. Bunlardan birincisi ölçülen verilerden alarmların üretilmesidir. Burada daha çokEKG verisinden alarm üretilmesine yogunlasılmıstır. Ikincisi ise, EKG aritmilerininsınıflandırılması ile olusturulan karar destek yazılımıdır.This project involves development of a telemedicine system utilizing today's information andmobile communication technologies for remote monitoring of patients with chronic diseasessuch as diabetes, asthma, heart attacks and high blood pressure. Initialization of this study isbased on the publications of Eric TOPOL and The World Health Organization about theimportance of telemedicine and remote patient monitoring in 2010.An important part of the project involves to develop software and hardware for the emergingstandards IEEE 802.15.6, since the power consumptions and interference problems ofZigBee and Bluetooth technologies, which are main rivals for the health industry, are foundunsuitable for a business model of remote patient monitoring. IEEE 802.15.6 offers acommunication highway for health data in 2.36 to 2.4 GHz with 600 kHz of its channels.However there was no hardware and software to implement such a communication andsupport applications required for health industry based on IEEE 802.15.6 standards. So, animportant part of the project is dedicated to implement the required hardware including radioand software. The software also realizes machine to machine communication to implementdevice to device communication to collect health data, as a fashion of machine-to-machinecommunication. The aim of the project, which is obtain a cable equivalent reliablecommunication for health data, has been mostly achieved.Other two essential part of the projects are sensor devices to measure the health data anddecision making system from the measurements. The circuits for ECG, respiration, SPO2,blood-pressure, body temperature and acceleration sensors are developed in a way that theirperformances equivalent to devices used in an intensive care unit. And, a software forgenerating alarms and classification of the disease during the remote monitoring of patientsis developed in order to assist health personnel. The obtained accuracies are published inseveral conferences.Keywords: eHealth, telemedicine, tele-monitoring, IEEE 802.15.6, BAN, M2M, ECG,respiration, SPO2, blood-pessure