We aimed to identify and characterise behavioural profiles in patients at high risk of SCD, by using deep representation learning of day-to-day behavioural recordings. We present a pipeline that employed unsupervised clustering on low-dimensional representations of behavioural time-series data learned by a convolutional residual variational neural network (ResNet-VAE). Data from the prospective, observational SafeHeart study conducted at two large tertiary university centers in the Netherlands and Denmark were used. Patients received an implantable cardioverter-defibrillator (ICD) between May 2021 and September 2022 and wore wearable devices using accelerometer technology during 180 consecutive days. A total of 272 patients (mean age of 63.1 ± 10.2 years, 81% male) were eligible with a total sampling of 37,478 days of behavioural data (138 ± 47 days per patient). Deep representation learning identified five distinct behavioural profiles: Cluster A (n = 46) had very low physical activity levels and a disturbed sleep pattern. Cluster B (n = 70) had high activity levels, mainly at light-to-moderate intensity. Cluster C (n = 63) exhibited a high-intensity activity profile. Cluster D (n = 51) showed above-average sleep efficiency. Cluster E (n = 42) had frequent waking episodes and poor sleep. Annual risks of malignant ventricular arrhythmias ranged from 30.4% in Cluster A to 9.8% and 9.5% for Clusters D-E, respectively. Compared to low-risk profiles (D-E), Cluster A demonstrated a three-to-four fold increased risk of malignant ventricular arrhythmias adjusted for clinical covariates (adjusted HR 3.63, 95% CI 1.54–8.53, p < 0.001). These behavioural profiles may guide more personalised approaches to ventricular arrhythmia and SCD prevention.

我们的目的是通过使用日常行为记录的深度表征学习来识别和表征 SCD 高风险患者的行为特征。我们提出了一种管道，该管道对通过卷积残差变分神经网络（ResNet-VAE）学习的行为时间序列数据的低维表示采用无监督聚类。使用的数据来自在荷兰和丹麦的两个大型高等教育中心进行的前瞻性观察性 SafeHeart 研究。患者在 2021 年 5 月至 2022 年 9 月期间接受了植入式心律转复除颤器 (ICD)，并连续 180 天佩戴采用加速度计技术的可穿戴设备。共有 272 名患者（平均年龄为 63.1 ± 10.2 岁，81% 为男性）符合资格，总共采样了 37,478 天的行为数据（每位患者 138 ± 47 天）。深度表征学习确定了五种不同的行为特征：A 组（ n = 46）的体力活动水平非常低，睡眠模式受到干扰。簇 B ( n = 70) 具有较高的活动水平，主要是轻度到中等强度。簇 C ( n = 63) 表现出高强度的活动特征。 D 组（ n = 51）的睡眠效率高于平均水平。 E 组（ n = 42）有频繁的清醒发作和睡眠不佳。恶性室性心律失常的年风险范围分别为 A 簇的 30.4% 到 DE 簇的 9.8% 和 9.5%。与低风险概况 (DE) 相比，根据临床协变量调整后，集群 A 的恶性室性心律失常风险增加了三到四倍（调整后的 HR 3.63，95% CI 1.54–8.53， p < 0.001）。 这些行为特征可以指导更个性化的室性心律失常和心源性猝死预防方法。