Atrial fibrillation (AF) and congestive heart failure (CHF) are the most common cardiac diseases and are potentially life-threatening . In 2017, AF affected 37.574 million people worldwide, and its frequency has risen by 33% in the last 20 years . The number of AF cases is expected to increase by more than 60% by 2050 . Similarly, the global prevalence of heart failure in the year 2017 was 64.34 million people . The lifetime risk of developing CHF is over 20% after the age of 40 years . Undiagnosed AF and CHF can endanger the patient’s life ; therefore, prompt diagnosis is important.
Electrocardiogram (ECG) is a non-invasive method that records the electrical activity of the heart which is commonly measured and analyzed by researchers . ECG signals can be used to detect abnormalities in the heart, such as cardiac arrhythmias  or heart failure. Therefore, ECG is important to represent objectively the occurrence of AF and CHF. Several studies have proposed algorithms to classify various cardiac diseases including AF and CHF based on ECG signals.
Rizal et al. analyzed ECG signals based on parameters of the Hjorth descriptor method such as activity, mobility, and complexity [7,8]. A study conducted in 2015 using various classifier algorithms such as k-mean clustering, k-nearest neighbor, and multi-layer perceptron reported an accuracy of 88.67%, 99.3%, and 99.3% respectively to classify AF, CHF, and NSR. The same researchers reported an accuracy of 94% in a study conducted in 2017 using the higher order complexity and k-nearest neighbor classifier .
Furthermore, Thaweesak et al. used the primary datasets of AF, CHF, and NSR which consisted of 90 recordings. Their findings indicated that the Hjorth descriptor is capable of class separation among cardiac arrhythmia patient groups and reported accuracy rates of 84.89%, 88.22%, and 76 % using the least-squares, maximum likelihood, and support vector machine respectively .
The most challenging aspect of ECG analysis is feature extraction. The aforementioned studies [7–9], extracted the features of ECG signals using the Hjorth descriptor method. However, owing to noise interference, some features did not perfectly represent the characteristics of ECG signals. Therefore, the classification systems used in the previous studies remain prone to false detection that decreases the accuracy performance.
Recently, convolutional neural networks (CNNs) have been identified as potential approaches for ECG classification. CNNs automatically extract features from the raw ECG signal data. Several studies have applied CNNs to classify cardiac diseases and have shown promising results. Ping et al. reported the highest f1-scores value of 89.55% using a CNN and long short-term memory to classify AF and normal conditions . Sidrah et al. reported a classification accuracy of 86.5% using a CNN and long short- term memory to classify AF and normal conditions . Similarly, Georgios et al. reported sensitivity of 97.87% and specificity of 99.29% using a CNN and long short-term memory to classify AF and normal conditions . Moreover, Nurmaini et al. reported a classification accuracy of 99.17% using a CNN to classify three conditions, including normal, AF, and non-AF conditions .
As well as several studies developed automated systems for detecting AF, Wang et al. reported a classification accuracy of 99.85% using a CNN and long short-term memory to classify CHF and normal conditions . Ning et al. reported a classification accuracy of 99.3% using a recursive neural network to classify CHF and normal conditions . Meanwhile, Porumb et al. reported accuracy of 100 % using a CNN to classify CHF and normal conditions .
The aforementioned studies [10–16] developed the automatic system detection for AF or CHF conditions separately. Furthermore, Padmavati et al. proposed a classification system to identify four classes of cardiac disease including atrial fibrillation (AF), myocardial infarction (MI), congestive heart failure (CHF), and normal conditions . The study reported a classification accuracy of 80.1% to classify CHF and normal, 85.9% of accuracy to classify MI and CHF, and 65.4% of accuracy to classify MI and normal conditions. However, the accuracy performance of their proposed model decreased up to 63.5% and 31.2% while extended to classify three classes (CHF, MI, and normal) and four classes (AF, CHF, MI, and normal) respectively.
Performance results of certain previous studies on the classification of ECG signals revealed that conventional machine learning methods were sensitive to noise; therefore, data cleaning was required. In contrast to the conventional approaches that required separate dataset preprocessing, feature extraction, and classification processes, CNNs can directly extract features from raw input. As a result, in the presence of sufficient training samples, the features extracted by a CNN model would be more detailed than those extracted manually.
According to the advantages of CNNs, some limitations which previously existed in conventional machine learning can be overcome with CNNs. Therefore, a one-dimensional (1-D) CNN was proposed to design an optimal ECG classification system that can improve the performance accuracy of the previous methods. The aim of this study was to investigate the performance of the 1-D CNN to classify raw ECG signals of AF, CHF, and NSR.