Deep Learning-based Arrhythmia Detection -A Deep-Belief Network Approach

Source: Deep Learning on Medium

Deep Learning-based Arrhythmia Detection -A Deep-Belief Network Approach

Introduction

According to the World Health Organization surveys, heart diseases are one of the most important reasons which cause death. Heart disease symptoms depend on what type of heart disease you have. An electrocardiogram (ECG) is a nonlinear and non-stationary diagnostic signal that is important for cardiac disorders.

Arrhythmia is a problem concerning the abnormal rhythm and rate of heartbeats. The heart can beat too fast, too slowly, or inconsistently in different types of arrhythmias, which may feel like antagonism affection or fluttering. Several types of arrhythmia are harmless, but some of them refer the cardiac disorders that may cause death. The ECG is a popular diagnosis tool which is of the primary importance for cardiologists.

Deep Learning Approach

Deep learning (DL) is an effective and high-performance machine learning algorithm which is gaining popularity. Frequently used analyses of the DL are used in image processing, speech and natural language processing processes. In this analysis, Deep Belief Networks (DBN), which is an adaptable DL algorithm, is utilized to classify the heartbeats from different classes of arrhythmia using ECG waveform as the input of the structure.

The Data Set Used

In this analysis, the MIT-BIH arrhythmia database (MADB) is utilized.This database has been used for evaluating arrhythmia detection and classifying the arrhythmia types. MADB contains 48 long-term ECGs from 25 men aged 32–89 years, and 22 women aged 23–89 years; each has 11-bit resolution with 360 Hz sampling frequency. The heartbeats are labelled as five main arrhythmia types defined by the Association for the Advancement of Medical Instruments (AAMI) standard

AAMI classifies heartbeats into normal beats (N), supraventricular ectopic heartbeats (S), ventricular ectopic heartbeats (V), fusion heartbeats (F), and unknown heartbeats (Q).

Deep Belief Networks

This approach introduces deep learning (DL) application for automatic arrhythmia classification. The proposed model consists of a multi-stage classification system of raw ECG using DL algorithms. The DBN is one of the most effective DL algorithms which has a greedy layer-wise training phase [. The DBN is composed of both Restricted Boltzmann Machines (RBM) or an autoencoder based layer-by-layer unsupervised pre-training procedure and neural network-based supervised training. Considering RBM with input layer activations ‘v’(for visible units) and hidden layer activations ℎ (hidden units), the bias of the visible unit unit ‘b’ ,bias of hidden unit ‘c’.

Representing Equation

Results and Analysis

The training set of the DBN-based automatic arrhythmia classification model includes 4,077 of ECG waveforms from various types of heartbeat classes distributed homogeneously. The DBN-based multistage model is tested using 2,000 of ECG waveforms.

The ECG Wave Form With Faulty Datapoints

Faulty Data Point ECG Wave

The proposed multistage DBN model separates N, S, V, F, and Q types of arrhythmias, respectively. Four of the DBN models are used in the proposed system. The RBM based greedy layer-wise pre-training is used in this model at the unsupervised learning stages of all DBNs with 5 epochs. The parameters of the RBM were denoted by iterations. The models were tested with a limited number of the parameters and the highest classification performances are given. The learning rate of the model is 3 and the softmax output function was utilized constantly. The proposed multistage arrhythmia classification model consists of 4 DBN structures with various numbers of hidden units. The DBN1 has 2 hidden layers with 100–260 hidden units; the DBN2 has 3 hidden layers with 230–520–210 hidden units; the DBN3 has 2 hidden layers with 120–240 hidden units; and the DBN4 has 2 hidden layers with 70–190 hidden units. The four DBN structures are connected sequentially and have the ability to separate five classes of arrhythmia types defined by ANSI/AAMI.

Conclusion and AI model Accuracy

The waves plot a regular form in normal sinus rhythm. Any obvious changes occurring in indicates the irregularity or arrhythmia in heartbeats.

The meaningful data points for arrhythmia classification are thickened between S-T waves and P-Q waves for the proposed DBN-based multistage classification model

The proposed DBN-based Arrhythmia classification has discriminated five types of heartbeats with a very highest accuracy of 95.05%. This achievement proves the success and efficiency of the Deep Belief Network algorithm in raw ECG signals for Cardiac Health Conditions.