A Deviation Analysis Framework for ECG Signals Using Controlled Spatial Transformation

Abstract

An important paradigm in smart health is developing diagnosis tools and monitoring a patient's heart activity through processing Electrocardiogram (ECG) signals is a key example, sue to high mortality rate of heart-related disease. However, current heart monitoring devices suffer from two important drawbacks: i) failure in capturing inter-patient variability, and ii) incapability of identifying heart abnormalities ahead of time to take effective preventive and therapeutic interventions.This paper proposed a novel predictive signal processing method to solve these issues. We propose a two-step classification framework for ECG signals, where a global classifier recognizes severe abnormalities by comparing the signal against a universal reference model. The seemingly normal signals are then passed through a personalized classifier, to recognize mild but informative signal morphology distortions. The idea is to develop a patient-specific reference for normal heart function. Another key contribution is developing a novel deviation analysis based on a controlled nonlinear transformation (with two computational and analytical optimization methods) to capture significant deviations of the signal towards any of predefined abnormality classes. Here, we embrace the proven but overlooked fact that certain features of ECG signals reflect underlying cardiac abnormalities before the occurrences of cardiac disease. The proposed method achieves a classification accuracy of 96.6% and provides a unique feature of predictive analysis by providing warnings before critical heart conditions. In particular, the chance of observing a severe problem (in terms of a red alarm) is raised by about 5% to 10% after observing a yellow alarm of the same type. Although we used this methodology to provide early precaution messages to elderly and high-risk heart-patients, the proposed method is general and applicable to similar bio-medical signal processing applications.

SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach

“…First, the hand-engineered feature-based methods that require a prior knowledge of EEG analysis to extract the most relevant features. These approaches first extract the most common features such as time, frequency and time-frequency domain features [5], [6], [7], [8] of single channel-EEG waveforms. Then, they apply conventional machine learning algorithms such as support vector machines (SVM) [9], random forests [10], [11] and neural networks [12] to train the model for sleep stage classification based on the extracted features.…”

Inter- and Intra- Patient ECG Heartbeat Classification for Arrhythmia Detection: A Sequence to Sequence Deep Learning Approach

“…This calls for automatic heartbeat classification methods that are able to diagnose arrhythmic heartbeats in real-time with high accuracy. Several machine learning algorithms such as support vector machines (SVM), multilayer perceptron (MLP), reservoir computing with logistic regression (RC) and decision trees have been utilized for arrhythmia detection [1], [2], [3], [4], [5], [6], [7], [8]. These shallow machine learning methods for ECG processing usually follow three main steps, including 1) signal pre-processing, which includes noise removal methods, heartbeat segmentation, etc; 2) feature extraction; and 3) learning/classification.…”