Gendarmerie and Coast Guard Academy, Ankara 06805, Turkey.
Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Kafkas University, Kars 36100, Turkey.
Sensors (Basel). 2023 Jun 23;23(13):5835. doi: 10.3390/s23135835.
Heart sounds have been extensively studied for heart disease diagnosis for several decades. Traditional machine learning algorithms applied in the literature have typically partitioned heart sounds into small windows and employed feature extraction methods to classify samples. However, as there is no optimal window length that can effectively represent the entire signal, windows may not provide a sufficient representation of the underlying data. To address this issue, this study proposes a novel approach that integrates window-based features with features extracted from the entire signal, thereby improving the overall accuracy of traditional machine learning algorithms. Specifically, feature extraction is carried out using two different time scales. Short-term features are computed from five-second fragments of heart sound instances, whereas long-term features are extracted from the entire signal. The long-term features are combined with the short-term features to create a feature pool known as long short-term features, which is then employed for classification. To evaluate the performance of the proposed method, various traditional machine learning algorithms with various models are applied to the PhysioNet/CinC Challenge 2016 dataset, which is a collection of diverse heart sound data. The experimental results demonstrate that the proposed feature extraction approach increases the accuracy of heart disease diagnosis by nearly 10%.
几十年来，心音一直被广泛研究用于心脏病诊断。文献中应用的传统机器学习算法通常将心音分成小窗口，并采用特征提取方法对样本进行分类。然而，由于没有能够有效表示整个信号的最优窗口长度，因此窗口可能无法充分表示潜在的数据。为了解决这个问题，本研究提出了一种新方法，将基于窗口的特征与从整个信号中提取的特征相结合，从而提高传统机器学习算法的整体准确性。具体来说，使用两种不同的时间尺度进行特征提取。从心音实例的五秒钟片段中计算短期特征，而从整个信号中提取长期特征。将长期特征与短期特征相结合，创建一个名为长短期特征的特征池，然后用于分类。为了评估所提出方法的性能，将各种传统机器学习算法与各种模型应用于 PhysioNet/CinC Challenge 2016 数据集，该数据集包含各种心音数据。实验结果表明，所提出的特征提取方法将心脏病诊断的准确性提高了近 10%。
