Mobile ECG acquisition device for early diagnosis based on Pan-Tompkins algorithm

In recent years, the usage of mobile electrocardiogram (ECG) devices has drawn much attention not only to in house patients but to home patients as well. The devices are truly useful for cardiac patients who need continuous monitoring while they are engaged in daily activities. The portable ECG devices particularly facilitate real time ECG recording and analysis for further examination by the doctors. This project focuses on implementing a mobile ECG acquisition device using Arduino UNO, Bluetooth HC-05 and AD8232 Heart Rate Monitor. Pan-Tompkins algorithm is used for QRS complex detection in order to classify the ECG signals either as normal or abnormal that is useful for early diagnosis. The goal of interest is to obtain a correct detection of QRS complex with high accuracy. Thus, the Pan-Tompkins algorithm is suitable as it is a well-known, simple yet efficient method in detecting QRS complexes accurately. The device acquires a Bluetooth technology to send raw data of ECG signal to Android smartphone. The ECG signals are displayed on the mobile interface and then the ECG signal analysis will be carried out by developing the Java-based Android application. The application will offer ECG processing techniques including R-R interval and QRS duration parameter extraction analysis. This device provides three ECG electrodes using Lead II placement for recording. The traces of the ECG leads are then plotted by the app. After that, the ECG data are saved as text files in the phone storage. The users also can view their history records of the previous ECG recording. The mobile app can capture and plot the incoming ECG signals from the remote device. The results shown that for a normal ECG signals, it will have the following parameters; heart rate of 60 to 100 beats per second, R-R interval duration of 0.4s to 1.2s, and QRS duration of 0.06s to 0.10s; else it will considered as abnormal signal

Development of ECG and EMG platform with IMU to eliminate the motion artifacts found in measurements

The long term measurement and analysis of electrophysiological parameters is crucial for diagnosis of chronic diseases, and to monitor critical health parameters. It is also very important to monitor physical fitness improvement, or degradation level, of human beings where physical fitness is entirely critical for their work, or of more vulnerable members of society such as senior citizens and the sick. The state-of-the-art technological developments are leading to the use of artificial intelligence in the continuous monitoring and identification of life-threatening events in the daily life of ordinary people. However, these ambulatory measurements of electrophysiological parameters leads to drastic motion artifacts because of the test subject’s movements. Therefore, there is a dire need for the development of both hardware and software solutions to address this challenge. The scope of this thesis is to develop a hardware platform, by using off-the-shelf discrete and IC electronic components, to measure two electrophysiological parameters, electrocardiogram (ECG) and electromyogram (EMG), with an additional motion sensor inertial measurement unit (IMU) comprising nine degrees of freedom. The ECG, EMG and IMU data will be collected using the developed measurement platform from various predefined day-to-day routine activity events. A Bluetooth interface will be developed to transmit the data wirelessly, and record it on a laptop for further real-time processing. The resources of the electrical workshop and measurement lab at Aalto University will be used for the development, assembly, testing and finally for research of the measurement platform. The second aspect of the study is to prepare, process and analyze the recorded ECG and EMG data by using MATLAB. Various filtering, denoising, processing and analysis algorithms will be developed and executed to extract the features of the ECG and EMG waveform structures. Finally, graphical representations will be made for the resulting outputs of the aforementioned techniques

Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

Implementation of optimized low pass filter for ECG filtering using verilog

Electrocardiogram is a standard method used for the diagnosis of heart related disease. QRS complex plays an important role in Electrocardiogram signal processing since it is the prominent feature of Electrocardiogram signal. One of the important modules in the QRS detection algorithm is filtering. Electrocardiogram signal is processed to filter out unwanted signal through digital filtering. The main objective of this paper is to compare the resource utilization of hardware realization consumed between Direct Form I structure and Direct Form II structure. In this work, Infinite Impulse Response low pass filter to remove high frequency noise is designed with a passband frequency and stopband frequency of 5 and 25 Hz respectively. The designed filter is verified using Matlab Filter Design Analysis tool and realized in hardware using Verilog. Both the results show that the unwanted signals in the raw ECG signal are attenuated through the designed filter. The resource utilization result shows improvement with optimized Direct Form II implementation. The amount of look up tables, flip flop and digital signal processing used with Direct Form II structure shows a reduction to 0.26%, 0.12% and 2.50% respectively compared to 1.17%, 0.20%, 2.92% of utilization with Direct Form I structure

Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference

Electronic devices and systems for monitoring of diabetes and cardiovascular diseases

Diabetes is a serious chronic disease which causes a high rate of morbidity and mortality all over the world. In 2007, more than 246 million people suffered from diabetes worldwide and unfortunately the incidence of diabetes is increasing at alarming rates. The number of people with diabetes is expected to double within the next 25 years due to a combination of population ageing, unhealthy diets, obesity and sedentary lifestyles. It can lead to blindness, heart disease, stroke, kidney failure, amputations and nerve damage. In women, diabetes can cause problems during pregnancy and make it more likely for the baby to be born with birth defects. Moreover, statistical analysis shows that 75% of diabetic patients die prematurely of cardiovascular disease (CVD). The absolute risk of cardiovascular disease in patients with type 1 (insulin-dependent) diabetes is lower than that in patients with type 2 (non-insulin-dependent) diabetes, in part because of their younger age and the lower prevalence of CVD risk factors, and in part because of the different pathophysiology of the two diseases. Unfortunately, about 9 out of 10 people with diabetes have type 2 diabetes. For these reasons, cardiopathes and diabetic patients need to be frequently monitored and in some cases they could easily perform at home the requested physiological measurements (i.e. glycemia, heart rate, blood pressure, body weight, and so on) sending the measured data to the care staff in the hospital. Several researches have been presented over the last years to address these issues by means of digital communication systems. The largest part of such works uses a PC or complex hardware/software systems for this purpose. Beyond the cost of such systems, it should be noted that they can be quite accessible by relatively young people but the same does not hold for elderly patients more accustomed to traditional equipments for personal entertainment such as TV sets. Wearable devices can permit continuous cardiovascular monitoring both in clinical settings and at home. Benefits may be realized in the diagnosis and treatment of a number of major 15 diseases. In conjunction with appropriate alarm algorithms, they can increase surveillance capabilities for CVD catastrophe for high-risk subjects. Moreover, they could play an important role in the wireless surveillance of people during hazardous operations (military, fire-fighting, etc.) or during sport activities. For patients with chronic cardiovascular disease, such as heart failure, home monitoring employing wearable device and tele-home care systems may detect exacerbations in very early stages or at dangerous levels that necessitate an emergency room visit and an immediate hospital admission. Taking into account mains principles for the design of good wearable devices and friendly tele-home care systems, such as safety, compactness, motion and other disturbance rejection, data storage and transmission, low power consumption, no direct doctor supervision, it is imperative that these systems are easy to use and comfortable to wear for long periods of time. The aim of this work is to develop an easy to use tele-home care system for diabetes and cardiovascular monitoring, well exploitable even by elderly people, which are the main target of a telemedicine system, and wearable devices for long term measuring of some parameters related to sleep apnoea, heart attack, atrial fibrillation and deep vein thrombosis. Since set-top boxes for Digital Video Broadcast Terrestrial (DVB-T) are in simple computers with their Operating System, a Java Virtual Machine, a modem for the uplink connection and a set of standard ports for the interfacing with external devices, elderly, diabetics and cardiopathes could easily send their self-made exam to the care staff placed elsewhere. The wearable devices developed are based on the well known photopletysmographic method which uses a led source/detector pair applied on the skin in order to obtain a biomedical signal related to the volume and percentage of oxygen in blood. Such devices investigate the possibility to obtain more information to those usually obtained by this technique (heart rate and percentage of oxygen saturation) in order to discover new algorithms for the continuous and remote or in ambulatory monitoring and screening of sleep apnoea, heart attack, atrial fibrillation and deep vein thrombosis

ECG analysis and classification using CSVM, MSVM and SIMCA classifiers

Reliable ECG classification can potentially lead to better detection methods and increase accurate diagnosis of arrhythmia, thus improving quality of care. This thesis investigated the use of two novel classification algorithms: CSVM and SIMCA, and assessed their performance in classifying ECG beats. The project aimed to introduce a new way to interactively support patient care in and out of the hospital and develop new classification algorithms for arrhythmia detection and diagnosis. Wave (P-QRS-T) detection was performed using the WFDB Software Package and multiresolution wavelets. Fourier and PCs were selected as time-frequency features in the ECG signal; these provided the input to the classifiers in the form of DFT and PCA coefficients. ECG beat classification was performed using binary SVM. MSVM, CSVM, and SIMCA; these were subsequently used for simultaneously classifying either four or six types of cardiac conditions. Binary SVM classification with 100% accuracy was achieved when applied on feature-reduced ECG signals from well-established databases using PCA. The CSVM algorithm and MSVM were used to classify four ECG beat types: NORMAL, PVC, APC, and FUSION or PFUS; these were from the MIT-BIH arrhythmia database (precordial lead group and limb lead II). Different numbers of Fourier coefficients were considered in order to identify the optimal number of features to be presented to the classifier. SMO was used to compute hyper-plane parameters and threshold values for both MSVM and CSVM during the classifier training phase. The best classification accuracy was achieved using fifty Fourier coefficients. With the new CSVM classifier framework, accuracies of 99%, 100%, 98%, and 99% were obtained using datasets from one, two, three, and four precordial leads, respectively. In addition, using CSVM it was possible to successfully classify four types of ECG beat signals extracted from limb lead simultaneously with 97% accuracy, a significant improvement on the 83% accuracy achieved using the MSVM classification model. In addition, further analysis of the following four beat types was made: NORMAL, PVC, SVPB, and FUSION. These signals were obtained from the European ST-T Database. Accuracies between 86% and 94% were obtained for MSVM and CSVM classification, respectively, using 100 Fourier coefficients for reconstructing individual ECG beats. Further analysis presented an effective ECG arrhythmia classification scheme consisting of PCA as a feature reduction method and a SIMCA classifier to differentiate between either four or six different types of arrhythmia. In separate studies, six and four types of beats (including NORMAL, PVC, APC, RBBB, LBBB, and FUSION beats) with time domain features were extracted from the MIT-BIH arrhythmia database and the St Petersburg INCART 12-lead Arrhythmia Database (incartdb) respectively. Between 10 and 30 PCs, coefficients were selected for reconstructing individual ECG beats in the feature selection phase. The average classification accuracy of the proposed scheme was 98.61% and 97.78 % using the limb lead and precordial lead datasets, respectively. In addition, using MSVM and SIMCA classifiers with four ECG beat types achieved an average classification accuracy of 76.83% and 98.33% respectively. The effectiveness of the proposed algorithms was finally confirmed by successfully classifying both the six beat and four beat types of signal respectively with a high accuracy ratio

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