The performance of a low-power single-lead armband in generating electrocardiogram (ECG) signals from the chest and left arm was validated against a BIOPAC MP160 benchtop system in real-time. The filtering performance of three adaptive filtering algorithms, namely least mean squares (LMS), recursive least squares (RLS), and extended kernel RLS (EKRLS) in removing white (W), power line interference (PLI), electrode movement (EM), muscle artifact (MA), and baseline wandering (BLW) noises from the chest and left-arm ECG was evaluated with respect to the mean squared error (MSE). Filter parameters of the used algorithms were adjusted to ensure optimal filtering performance. LMS was found to be the most effective adaptive filtering algorithm in removing all noises with minimum MSE. However, for removing PLI with a maximal signal-to-noise ratio (SNR), RLS showed lower MSE values than LMS when the step size was set to 1 × 10⁻⁵. We proposed a transformation framework to convert the denoised left-arm and chest ECG signals to their low-MSE and high-SNR surrogate chest signals. With wide applications in wearable technologies, the proposed pipeline was found to be capable of establishing a baseline for comparing left-arm signals with original chest signals, getting one step closer to making use of the left-arm ECG in clinical cardiac evaluations.

中文翻译：

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嘈杂的左臂ECG自适应滤波并将其转换为替代胸部信号的管道

相对于BIOPAC MP160台式系统，实时验证了低功率单导臂臂在从胸部和左臂产生心电图（ECG）信号方面的性能。三种自适应滤波算法的滤波性能，即最小均方（LMS），递归最小二乘（RLS）和扩展核RLS（EKRLS）在去除白光（W），电源线干扰（PLI），电极运动（EM）方面相对于均方误差（MSE），评估了胸部和左臂ECG的肌肉伪影（MA）和基线漂移（BLW）噪声。调整了所用算法的过滤参数，以确保最佳的过滤性能。LMS被发现是以最小的MSE消除所有噪声的最有效的自适应滤波算法。但是，为了去除具有最大信噪比（SNR）的PLI，⁻⁵。我们提出了一种转换框架，可将降噪后的左臂和胸部ECG信号转换为低MSE和高SNR替代胸部信号。随着可穿戴技术的广泛应用，所提议的管道能够建立基线，以将左臂信号与原始胸部信号进行比较，与在临床心脏评估中使用左臂心电图的距离更近了一步。