Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, 3-1-1 Tsushimanaka, Kita-Ku, Okayama 700-8530, Japan.
Faculty of Computer Science, Brawijaya University, Malang 65145, Indonesia.
Sensors (Basel). 2022 Jul 6;22(14):5080. doi: 10.3390/s22145080.
Heartbeat monitoring may play an essential role in the early detection of cardiovascular disease. When using a traditional monitoring system, an abnormal heartbeat may not appear during a recording in a healthcare facility due to the limited time. Thus, continuous and long-term monitoring is needed. Moreover, the conventional equipment may not be portable and cannot be used at arbitrary times and locations. A wearable sensor device such as Polar H10 offers the same capability as an alternative. It has gold-standard heartbeat recording and communication ability but still lacks analytical processing of the recorded data. An automatic heartbeat classification system can play as an analyzer and is still an open problem in the development stage. This paper proposes a heartbeat classifier based on RR interval data for real-time and continuous heartbeat monitoring using the Polar H10 wearable device. Several machine learning and deep learning methods were used to train the classifier. In the training process, we also compare intra-patient and inter-patient paradigms on the original and oversampling datasets to achieve higher classification accuracy and the fastest computation speed. As a result, with a constrain in RR interval data as the feature, the random forest-based classifier implemented in the system achieved up to 99.67% for accuracy, precision, recall, and F1-score. We are also conducting experiments involving healthy people to evaluate the classifier in a real-time monitoring system.
心跳监测在心血管疾病的早期检测中可能发挥重要作用。在使用传统监测系统时，由于医疗机构记录时间有限，心跳异常可能不会在记录中出现。因此，需要进行连续和长期监测。此外，传统设备可能不便于携带，无法在任意时间和地点使用。Polar H10 等可穿戴传感器设备则提供了一种替代方案，它具有金标准的心跳记录和通信能力，但仍然缺乏对记录数据的分析处理。自动心跳分类系统可以作为分析器，这仍然是开发阶段的一个开放性问题。本文提出了一种基于 RR 间隔数据的心跳分类器，用于使用 Polar H10 可穿戴设备进行实时和连续的心跳监测。使用了几种机器学习和深度学习方法来训练分类器。在训练过程中，我们还比较了原始数据集和过采样数据集的患者内和患者间范例，以实现更高的分类准确性和最快的计算速度。结果表明，在 RR 间隔数据作为特征的约束下，系统中实现的基于随机森林的分类器在准确性、精度、召回率和 F1 分数方面达到了 99.67%。我们还在进行涉及健康人的实验，以在实时监测系统中评估分类器。
