A Patient-Adaptive Profiling Scheme for ECG Beat Classification

Recent trends in clinical and telemedicine applications highly demand automation in electrocardiogram (ECG) signal processing and heart beat classification. A patient-adaptive cardiac profiling scheme using repetition-detection concept is proposed in this paper. We first employ an efficient wavelet-based beat-detection mechanism to extract precise fiducial ECG points. Then, we implement a novel local ECG beat classifier to profile each patient's normal cardiac behavior. ECG morphologies vary from person to person and even for each person, it can vary over time depending on the person's physical condition and/or environment. Having such profile is essential for various diagnosis (e.g., arrhythmia) purposes. One application of such profiling scheme is to automatically raise an early warning flag for the abnormal cardiac behavior of any individual. Our extensive experimental results on the MIT-BIH arrhythmia database show that our technique can detect the beats with 99.59% accuracy and can identify abnormalities with a high classification accuracy of 97.42%

ECG-Based Cardiac Assessment for Microgravity and High-Altitude Atmospheres

This research project focuses on the electrocardiogram (ECG) signal characteristics and introduces novel methods to identify certain types of arrhythmia and/or the onset of heart attack with high accuracy. This is especially important as fatal heart episodes have been reported in connection with takeoffs and landings as well as high-altitude atmospheres. Signal processing techniques will be employed to identify ECG characteristic feature points and then machine learning will be applied to classify the signal into healthy or classes of irregular ECG beats. The proposed techniques are intended to conveniently assist monitoring the heart functionality in conditions such as aerospace environments

HEART MONITORING VIA WIRELESS ECG

The monitoring of heart had been a complex task. Acquiring ECG of the chronic patient spending most of their time outside the hospital had been a trivial task. Recording of ECG of such patients using wireless method is further challenging. This paper presents various methods of wireless ECG acquisition, their limitations and challenges. Cardiomobile, Flexible wireless ECG are the examples of such systems that are available in the medical world for wireless ECG

Optimal Multi-Stage Arrhythmia Classification Approach

Arrhythmia constitutes a problem with the rate or rhythm of the heartbeat, and an early diagnosis is essential for the timely inception of successful treatment. We have jointly optimized the entire multi-stage arrhythmia classification scheme based on 12-lead surface ECGs that attains the accuracy performance level of professional cardiologists. The new approach is comprised of a three-step noise reduction stage, a novel feature extraction method and an optimal classification model with finely tuned hyperparameters. We carried out an exhaustive study comparing thousands of competing classification algorithms that were trained on our proprietary, large and expertly labeled dataset consisting of 12-lead ECGs from 40,258 patients with four arrhythmia classes: atrial fibrillation, general supraventricular tachycardia, sinus bradycardia and sinus rhythm including sinus irregularity rhythm. Our results show that the optimal approach consisted of Low Band Pass filter, Robust LOESS, Non Local Means smoothing, a proprietary feature extraction method based on percentiles of the empirical distribution of ratios of interval lengths and magnitudes of peaks and valleys, and Extreme Gradient Boosting Tree classifier, achieved an F1-Score of 0.988 on patients without additional cardiac conditions. The same noise reduction and feature extraction methods combined with Gradient Boosting Tree classifier achieved an F1-Score of 0.97 on patients with additional cardiac conditions. Our method achieved the highest classification accuracy (average 10-fold cross-validation F1-Score of 0.992) using an external validation data, MIT-BIH arrhythmia database. The proposed optimal multi-stage arrhythmia classification approach can dramatically benefit automatic ECG data analysis by providing cardiologist level accuracy and robust compatibility with various ECG data sources

A Novel Low Complexity On body CVD Classifier ASIC Design Methodology

Due to increasing rate of cardiac disorders in developed and developing countries Continuous on body monitoring of ECG signal using the concept of IoT and Body Sensor Network has become the necessity. In this work we are proposing a novel low complex, low power algorithm and architecturefor E.C.G. classification which can be incorporated in present era of IOT and Body Sensor Network. Rather than going for Artificial Intelligence based pattern matching and complex DSP algorithm we have used the simplicity of Hurst exponent and Haar wavelet for filtering out anomalous E.C.G. signals and normal ones

ECG Classification with an Adaptive Neuro-Fuzzy Inference System

Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases can be made through an Adaptive Neuro-Fuzzy Inference System (ANFIS) preprocessed by subtractive clustering. Six types of heartbeats are classified: normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. The goal is to detect important characteristics of an ECG signal to determine if the patient’s heartbeat is normal or irregular. The results from three trials indicate an average accuracy of 98.10%, average sensitivity of 94.99%, and average specificity of 98.87%. These results are comparable to two artificial neural network (ANN) algorithms: gradient descent and Levenberg Marquardt, as well as the ANFIS preprocessed by grid partitioning