In this article, a novel and an efficient methodology is presented for real-time monitoring of ECG signals. The method involves fast Fourier transform (FFT) based discrete wavelet transform (DWT) for extracting the features from the heartbeats which involves less computational complexity in terms of additions and multiplications operations for higher order filter lengths. These features extracted are recognized using particle swarm optimization (PSO) tuned twin support vector machines (TSVM) classifier. The TSVM classifier is four times faster than the standard SVM while the PSO technique is employed to gradually tune the classifier parameters to achieve more accuracy. The proposed methodology is implemented on IoT based microcontroller platform and validated on the benchmark Physionet data to classify 16 categories of ECG signals. Once an abnormality is detected, the platform generates a pop-up message as a warning and sends the information to a remote platform allowing hospitals to take preventive measures. The platform reported a higher overall accuracy of 95.68% than the existing studies. Further, such implementation can be utilized as a warning system in both homecare as well as tele-monitoring applications to continuously monitor the cardiac condition of a subject anywhere to the state-of-art heart disease diagnosis.

中文翻译：

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基于物联网的远程实时心脏活动监测平台

在本文中，提出了一种新颖且有效的方法来实时监测 ECG 信号。该方法涉及基于快速傅立叶变换 (FFT) 的离散小波变换 (DWT)，用于从心跳中提取特征，这在高阶滤波器长度的加法和乘法运算方面涉及较少的计算复杂性。使用粒子群优化 (PSO) 调谐双支持向量机 (TSVM) 分类器识别提取的这些特征。TSVM 分类器比标准 SVM 快四倍，同时采用 PSO 技术逐步调整分类器参数以达到更高的精度。所提出的方法在基于物联网的微控制器平台上实施，并在基准 Physionet 数据上进行验证，以对 16 类 ECG 信号进行分类。一旦检测到异常，平台会生成一个弹出消息作为警告，并将信息发送到远程平台，以便医院采取预防措施。该平台报告的总体准确率高于现有研究的 95.68%。此外，这种实现可以用作家庭护理和远程监控应用中的警告系统，以连续监控任何地方的受试者的心脏状况，以进行最先进的心脏病诊断。