Automated myocardial infarction diagnosis from ECG

In the present dissertation, an automated neural network-based ECG diagnosing system was designed to detect the presence of myocardial infarction based on the hypothesis that an artificial neural network-based ECG interpretation system may improve the clinical myocardial infarction. 137 patients were included. Among them 122 had myocardial infarction, but the remaining 15 were normal. The sensitivity and the specificity of present system were 92.2% and 50.7% respectively. The sensitivity was consistent with relevant research. The relatively low specificity results from the rippling of the low pass filtering. We can conclude that neural network-based system is a promising aid for the myocardial infarction diagnosis

Strategies for neural networks in ballistocardiography with a view towards hardware implementation

A thesis submitted for the degree of Doctor of Philosophy at the University of LutonThe work described in this thesis is based on the results of a clinical trial conducted by the research team at the Medical Informatics Unit of the University of Cambridge, which show that the Ballistocardiogram (BCG) has prognostic value in detecting impaired left ventricular function before it becomes clinically overt as myocardial infarction leading to sudden death. The objective of this study is to develop and demonstrate a framework for realising an on-line BCG signal classification model in a portable device that would have the potential to find pathological signs as early as possible for home health care. Two new on-line automatic BeG classification models for time domain BeG classification are proposed. Both systems are based on a two stage process: input feature extraction followed by a neural classifier. One system uses a principal component analysis neural network, and the other a discrete wavelet transform, to reduce the input dimensionality. Results of the classification, dimensionality reduction, and comparison are presented. It is indicated that the combined wavelet transform and MLP system has a more reliable performance than the combined neural networks system, in situations where the data available to determine the network parameters is limited. Moreover, the wavelet transfonn requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced. Overall, a methodology for realising an automatic BeG classification system for a portable instrument is presented. A fully paralJel neural network design for a low cost platform using field programmable gate arrays (Xilinx's XC4000 series) is explored. This addresses the potential speed requirements in the biomedical signal processing field. It also demonstrates a flexible hardware design approach so that an instrument's parameters can be updated as data expands with time. To reduce the hardware design complexity and to increase the system performance, a hybrid learning algorithm using random optimisation and the backpropagation rule is developed to achieve an efficient weight update mechanism in low weight precision learning. The simulation results show that the hybrid learning algorithm is effective in solving the network paralysis problem and the convergence is much faster than by the standard backpropagation rule. The hidden and output layer nodes have been mapped on Xilinx FPGAs with automatic placement and routing tools. The static time analysis results suggests that the proposed network implementation could generate 2.7 billion connections per second performance

The Application of Computer Techniques to ECG Interpretation

This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field

A HARDWARE-SOFTWARE CO-DESIGNED WEARABLE FOR REAL-TIME PHYSIOLOGICAL DATA COLLECTION AND SIGNAL QUALITY ASSESSMENT

In the future, Smart and Connected Communities (S&CC) will use distributed wireless sensors and embedded computing platforms to produce meaningful data that can help individuals, and communities. Here, we presented a scanner, a data reliability estimation algorithm and Electrocardiogram (ECG) beat classification algorithm which contributes to the S&CC framework .In part 1, we report the design, prototyping, and functional validation of a low-power, small, and portable signal acquisition device for these sensors. The scanner was fully tested, characterized, and validated in the lab, as well as through deployment to users homes. As a test case, we show results of the scanner measuring WRAP temperature sensors with relative error within the 0.01% range. The scanner measurement shows distinguish temperature of 1F difference and excellent linear dependence between actual and measured resistance (R2 = 0.998). This device hasdemonstrated the possibility of a small, low-power portable scanner for WRAP sensors.Additionally, we explored the statistical data reliability metric (DReM) to explain the quality of bio-signal quantitatively on a scale between 0.0 -1.0. As proof of concept, we analyzed the ECG signal. Our DReM prediction algorithm measures the reliability of the ECG signals effectively with low Root mean square error = 0.010 and Mean absolute error = 0.008 and coefficient of determination R2 value of 0.990. Finally, we tested our model against the opinions of three independent judges and presented R2 value to determine the agreement between judgments vs our prediction model.We concluded our contribution to the S&CC framework by analyzing ECG beat classification with a pipeline of classifiers that focuses on improving the models performance on identifying minority classes (ventricular ectopic beat, supraventricular ectopic beat). Moreover, we intended to minimize morphological distortion introduced due to indiscriminate use of filtering techniques on ECG signals. Our approach shows an average positive predictive value 95.21%, sensitivity of95.28%, and F-1 score 95.76% respectively

Advanced Information Processing Methods and Their Applications

This Special Issue has collected and presented breakthrough research on information processing methods and their applications. Particular attention is paid to the study of the mathematical foundations of information processing methods, quantum computing, artificial intelligence, digital image processing, and the use of information technologies in medicine

Detection of shockable heart rhythms with convolutional neural networks : Based on ECG spectrograms

Purpose Automated feature extraction combined with deep learning has had and continues to have a strong impact on the improvement and implementation of pattern recognition driven by machine learning. Systems without prior expertise about a problem but with the ability to iteratively learn strategies to solve problems, tend to outperform concepts of manual feature engineering in vari-ous fields. In ECG data analysis as well as in other medical domains, models based on manual feature extraction are tedious to develop, require scientific expertise, and are oftentimes not easily adaptive to variations of the problem to be solved. This work aims to examine automated feature extraction and classification of ECG data, specifically of shockable heart rhythms, with convolu-tional neural networks and residual neural networks. The precise and rapid determination of shockable cardiac conditions is a decisive step to improve the chances of survival for patients having a sudden cardiac arrest. Conventional, commercially available automated external defib-rillators (AEDs) deploy algorithms based on manual feature extraction. Approximately 1 out of 10 shockable conditions is not recognized by the AED. Consequently, strategies for improvement need to be explored. Methods 125 ECG recordings from four annotated cardiac arrhythmia databases (American Heart Association Database, Creighton University Tachyarrhythmia Database, MIT-BIH Arrhythmia Da-tabase, MIT-BIH Malignant Ventricular Arrhythmia Database) with a duration of 30 mins or 8 mins (Creighton University Tachyarrhythmia Database) per recording were processed. Shockable con-ditions are identified as ventricular tachycardia, ventricular fibrillation, and ventricular flutter. The 1 channel ECG recordings (modified limb lead II) were normalized to 250 Hz sampling frequency, high-pass filtered (1 Hz cutoff and 0.85 filter steepness), second order Butterworth low-pass fil-tered (30 Hz cutoff), and notch filtered at 50 Hz. Consistent wavelet transformation with 5 octaves, 20 voices per octave, and a time bandwidth product parameter of 50 was applied to generate greyscale spectrogram representations of the ECG data (pixel value range from 0 to 255). The recordings were segmented into 3 s segments. Data augmentation around the borders of shock-able episodes and along shockable episodes was carried out to create balanced datasets con-sisting of 60340 samples. 45% of samples in the balanced dataset contain shockable rhythms with more than 60% temporal prevalence within each sample. Conventional convolutional neural networks and residual neural networks with varying architectures and hyperparameter settings were trained and evaluated on balanced datasets (train/val/test: 70/15/15). The approach focused on examining a broader range of parameter settings and model architectures rather than optimiz-ing a specific configuration. The best performing model was evaluated in a 5-fold cross-validation. Exemplarily, a leave-one-subject-out cross-validation was deployed with 3 randomly chosen re-cordings, with the constraints that each subject must come from a different database and contain a different shockable condition. Results and Conclusion The best performing model was a residual neural network with 96 residual blocks. The 5-fold cross-validation results on average in an accuracy of 0.987, a sensitivity of 0.992 on shock-able rhythms, and a specificity of 0.984 for non-shockable rhythms on the test sets. The ROC AUC score is 0.998 on average. The 3-fold leave-one-subject-out cross-validation reaches on average an accuracy of 0.984, a sensitivity of 0.984, and a specificity of 0.980. The ROC AUC score reaches 0.997 on average. The analysis of misclassified segments reveals that the classi-fier performs less accurately on border segments containing a shockable and at least one non-shockable rhythm. While the test set contains 4.73% border segments, the set of misclassified samples includes 11.29% border segments. The label distributions of the test set and the set of misclassified samples show that segments annotated as “not defined” (ND) and “ventricular fibril-lation or flutter” (VF-VFL) are significantly more prevalent in the set of misclassified samples. Histogram analysis, referring to the mean pixel intensity of the spectrograms, indicates that the classifier works less accurately on spectrograms with mean pixel values below 2 (practically flat-line signals or signals with very small amplitude). The results indicate that it is possible to improve the analysis of ECG data by deploying automated feature detection combined with artificial neural networks. The methods presented in this work are not restricted to the detection of shockable cardiac arrhythmias, they likewise em-phasize the potential of machine learning in the domain of biosignal analysis and correlated med-ical data. In the next step, the approach needs to be verified on a broader database. The tech-nology can even help create more comprehensive databases of clinical ECG data by supporting automated annotation

Neonatal Seizure Detection Using Deep Convolutional Neural Networks

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method

Electrocardiogram Signal Analysis and Simulations for Non-Invasive Diagnosis - Model-Based and Data-Driven Approaches for the Estimation of Ionic Concentrations and Localization of Excitation Origins

Das Elektrokardiogramm (EKG) ist die Standardtechnik zur Messung der elektrischen Aktivität des Herzens. EKG-Geräte sind verfügbar, kostengünstig und erlauben zudem eine nichtinvasive Messung. Das ist insbesondere wichtig für die Diagnose von kardiovaskulären Erkrankungen (KVE). Letztere sind mit verursachten Kosten von 210 Milliarden Euro eine der Hauptbelastungen für das Gesundheitssystem in Europa und dort der Grund für 3,9 Millionen Todesfälle – dies entspricht 45% aller Todesfälle. Neben weiteren Risikofaktoren spielen chronische Nierenerkrankungen und strukturelle Veränderungen des Herzgewebes eine entscheidende Rolle für das Auftreten von KVE. Deshalb werden in dieser Arbeit zwei Pathologien, die in Verbindung zu KVE stehen, betrachtet: Elektrolytkonzentrationsveränderungen bei chronisch Nierenkranken und ektope Foki, die autonom Erregungen iniitieren. In beiden Projekten ist die Entwicklung von Methoden mithilfe von simulierten Signalen zur Diagnoseunterstützung das übergeordnete Ziel. Im ersten Projekt helfen simulierte EKGs die Signalverarbeitungskette zur EKG-basierten Schätzung der Ionenkonzentrationen von Kalium und Calcium zu optimieren. Die Erkenntnisse dieser Optimierung fließen in zwei patienten-spezifische Methoden zur Kaliumkonzentrationsschätzung ein, die wiederum mithilfe von Patientendaten ausgewertet werden. Die Methoden lieferten im Mittel einen absoluten Fehler von 0,37 mmol/l für einen patienten-spezifischen Ansatz und 0,48 mmol/l für einen globalen Ansatz mit zusätzlicher patienten-spezifischer Korrektur. Die Vorteile der Schätzmethoden werden gegenüber bereits existierender Ansätze dargelegt. Alle entwickelten Algorithmen sind ferner unter einer Open-Source-Lizenz veröffentlicht. Das zweite Projekt zielte auf die Lokalisierung von ektopen Foki mithilfe des EKGs ohne die Nutzung der individuellen Patientengeometrie. 1.766.406 simulierte EKG-Signale (Body Surface Potential Maps (BSPMs)) wurden zum Trainieren von zwei Convolutional Neural Networks (CNNs) erzeugt. Das erste CNN sorgt für die Schätzung von Anfang und Ende der Depolarisation der Ventrikel. Das zweite CNN nutzt die Information der Depolarisation im BSPM zur Schätzung des Erregungsurpsrungs. Der spezielle Aufbau des CNNs ermöglicht die Darstellung mehrerer Lösungen, wie sie durch Mehrdeutigkeiten im BSPM vorliegen können. Der kleinste Median des Lokalisierungsfehlers lag bei 1,54 mm für den Test-Datensatz der simulierten Signale, bzw. bei 37 mm für Patientensignale. Somit erlaubt die Kombination beider CNNs die verlässliche Lokalisierung von ektopen Foki auch anhand von Patientendaten, obwohl Patientendaten vorher nicht im Training genutzt wurden. Die Resultate dieser zwei Projekte demonstrieren, wie EKG-Simulationen zur Entwicklung und Verbesserung von EKG-Signalverarbeitungsmethoden eingesetzt werden und bei der Diagnosefindung helfen können. Zudem zeigt sich das Potential der Kombination von Simulationen und CNNs, um einerseits die zumeist raren klinischen Signale zu ersetzen und andererseits Modelle zu finden, die für mehrere Patienten/-innen gültig sind. Die vorgestellten Methoden bergen die Möglichkeit, die Diagnosestellungen zu beschleunigen und mit hoher Wahrscheinlichkeit den Therapieerfolg der Patienten zu verbessern