ECG signals contain a lot of information related to cardiac activity and play a critical role in diagnosing of heart disease. However, relying on health information monitoring with simple ECG measurement can lead to errors in health information analysis. Therefore, for accurate diagnosis and analysis, an algorithm that can efficiently process user’s measurement environment and activity information is required. In this paper, we propose a wearable ECG monitoring system based on Knowledge Discovery Computing using 3-axis acceleration sensor. The proposed system measures real-time cardiac information and activity information simultaneously to minimize errors in health information analysis through Knowledge Discovery Computing between the user’s environment information and abnormal ECG according to the measurement environment in everyday life. In addition, we implemented a packet transmission protocol to effectively transmit large amounts of data analyzed through Knowledge Discovery Computing to the base station. First, arrhythmia detection was performed using R-peak detection preprocessing and pattern matching algorithm. Also, a classification algorithm was implemented to classify activity types by utilizing an accelerometer in order to recognize the context surrounding the user. Information on the user’s vital signs and activity information can be used for more accurately determine arrhythmia in daily life. Also, variable packet generation protocol was designed for an effective transmission of data packets increased exponentially by long-term measurements and wireless data transfer. The variable packet generation protocol is efficient in limited wireless network environments, because it generate packets of the entire data only with case of abnormal cardiac rhythm and transmits minimal information for normal cardiac rhythm. In order to evaluate the performance of ECG monitoring system based on Knowledge Discovery Computing, we designed a 2-lead ECG measurement belt manufactured with conductive fiber to minimize user discomfort, and assessed the system performance in data packet transmission, data recovery, and arrhythmia detection in dynamic states in daily life. In static states, the posture detection was 100%, heart rate detection 99.8%, and CR (Compression Ratio) was 193.99 and correlation coefficient of 0.95 with commercial systems. In dynamic states, 96% detection rate and 59.14 of CR is identified. If arrhythmia is determined based only on ECG signals, it is difficult to differentiate an actual abnormal cardiac rhythm from an ECG signal altered due to motion. The experiments conducted in this study confirmed that Knowledge Discovery is possible in the dynamic state through the proposed system in daily life.

ECG信号包含许多与心脏活动有关的信息，并且在心脏病的诊断中起着至关重要的作用。但是，依靠健康信息监控和简单的ECG测量会导致健康信息分析中的错误。因此，为了进行准确的诊断和分析，需要一种能够有效处理用户的测量环境和活动信息的算法。在本文中，我们提出了一种基于知识发现计算的可穿戴式心电监护系统，该系统使用三轴加速度传感器。所提出的系统同时测量实时的心脏信息和活动信息，以根据日常生活中的测量环境，通过用户环境信息和异常ECG之间的知识发现计算，最大限度地减少健康信息分析中的错误。此外，我们实施了一种数据包传输协议，以有效地将通过知识发现计算分析的大量数据传输到基站。首先，使用R-peak检测预处理和模式匹配算法执行心律不齐检测。而且，实施分类算法以通过利用加速度计对活动类型进行分类，以便识别用户周围的环境。关于用户生命体征的信息和活动信息可用于更准确地确定日常生活中的心律失常。此外，可变数据包生成协议被设计用于通过长期测量和无线数据传输以指数方式增加的数据包有效传输。可变数据包生成协议在有限的无线网络环境中非常有效，因为它仅在心律异常的情况下才生成全部数据的数据包，并为正常的心律发送最少的信息。为了评估基于Knowledge Discovery Computing的ECG监测系统的性能，我们设计了由导电纤维制成的2导联ECG测量带，以最大程度地减少用户的不适感，并评估了数据包传输，数据恢复和心律不齐检测中的系统性能。在日常生活中处于动态状态。在静态下，与商用系统相比，姿势检测为100％，心率检测为99.8％，CR（压缩比）为193.99，相关系数为0.95。在动态状态下，识别出96％的检出率和59.14的CR。如果仅根据心电图信号确定心律失常，很难将实际的异常心律与由于运动而改变的ECG信号区分开。在这项研究中进行的实验证实，通过拟议的系统在日常生活中以动态状态进行知识发现是可能的。