Premature Ventricular Contraction Beat Classification via Hyperdimensional Computing
Premature Ventricular Contraction Beat Classification via Hyperdimensional Computing
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DOI：
10.1109/ieeeconf56349.2022.10052044
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Brandon W. Ung;Lulu Ge;K. Parhi
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文献类型：
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作者：
Brandon W. Ung;Lulu Ge;K. Parhi
超维计算（HD）是一种新兴的大脑启发范式，用于机器学习分类任务。它使用简单的操作来操作超长向量（超向量），从而实现快速学习、能源效率、抗噪能力和高度并行的分布式框架。HD计算已经在生物信号分类领域显示出显著的前景。本文使用MIT-BIH心律失常数据库中的数据，通过HD计算解决了组特异性室性早搏（PVC）搏动检测。提取时间、心率变异性（HRV）和频谱特征，并且使用最小冗余最大相关性（mRMR）来对用于分类的特征进行排序和选择。三种编码方法进行了探索映射到HD空间的功能。HD计算分类器可以实现97.7%准确度的PVC节拍检测准确度，而卷积神经网络（CNN）等计算复杂度更高的方法则可以实现99.4%的准确度。
Hyperdimensional computing (HD) is an emerging brain-inspired paradigm used for machine learning classification tasks. It manipulates ultra-long vectors-hypervectors- using simple operations, which allows for fast learning, energy efficiency, noise tolerance, and a highly parallel distributed framework. HD computing has shown a significant promise in the area of biological signal classification. This paper addresses group-specific premature ventricular contraction (PVC) beat detection with HD computing using the data from the MIT-BIH arrhythmia database. Temporal, heart rate variability (HRV), and spectral features are extracted, and minimal redundancy maximum relevance (mRMR) is used to rank and select features for classification. Three encoding approaches are explored for mapping the features into the HD space. The HD computing classifiers can achieve a PVC beat detection accuracy of 97.7 % accuracy, compared to 99.4% achieved by more computationally complex methods such as convolutional neural networks (CNNs).