The recognition of cardiac arrhythmia in minimal time is important to prevent sudden and untimely deaths. The proposed work includes a complete framework for analyzing the Electrocardiogram (ECG) signal. The three phases of analysis include 1) the ECG signal quality enhancement through noise suppression by a dedicated filter combination; 2) the feature extraction by a devoted wavelet design and 3) a proposed hidden Markov model (HMM) for cardiac arrhythmia classification into Normal (N), Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Premature Ventricular Contraction (PVC) and Atrial Premature Contraction (APC). The main features extracted in the proposed work are minimum, maximum, mean, standard deviation, and median. The experiments were conducted on forty-five ECG records in MIT BIH arrhythmia database and in MIT BIH noise stress test database. The proposed model has an overall accuracy of 99.7 % with a sensitivity of 99.7 % and a positive predictive value of 100 %. The detection error rate for the proposed model is 0.0004. This paper also includes a study of the cardiac arrhythmia recognition using an IoMT (Internet of Medical Things) approach.

在最短的时间内识别出心律不齐对于防止猝死和不及时死亡很重要。拟议的工作包括用于分析心电图（ECG）信号的完整框架。分析的三个阶段包括：1）通过专用滤波器组合的噪声抑制来增强ECG信号质量；2）通过专用小波设计提取特征； 3）为心律失常分类建议的隐马尔可夫模型（HMM），将其分为正常（N），右束支传导阻滞（RBBB），左束支传导阻滞（LBBB），室性早搏（PVC）和房性早搏（APC）。拟议工作中提取的主要特征是最小值，最大值，均值，标准差和中位数。实验在MIT BIH心律失常数据库和MIT BIH噪声压力测试数据库中的45条ECG记录上进行。所提出的模型的总体准确度为99.7％，灵敏度为99.7％，阳性预测值为100％。提出的模型的检测错误率为0.0004。本文还包括使用IoMT（医疗物联网）方法进行的心律失常识别研究。