Cardiovascular diseases are projected to remain the single leading cause of death globally. Timely diagnosis and treatment of these diseases are crucial to prevent death and dangerous complications. One of the important tools in early diagnosis of arrhythmias is analysis of electrocardiograms (ECGs) obtained from ambulatory long-term recordings. The design of novel patch-type ECG recorders has increased the accessibility of these long-term recordings. In many applications, it is furthermore an advantage for these devices that the recorded ECGs can be analyzed automatically in real time. The purpose of this study was therefore to design a novel algorithm for automatic heart beat detection, and embed the algorithm in the CE marked ePatch heart monitor. The algorithm is based on a novel cascade of computationally efficient filters, optimized adaptive thresholding, and a refined search back mechanism. The design and optimization of the algorithm was performed on two different databases: The MIT-BIH arrhythmia database ( $Se=99.90$ %, $P^{+}=99.87$ ) and a private ePatch training database ( $Se=99.88$ %, $P^{+}=99.37$ %). The offline validation was conducted on the European ST-T database ( $Se=99.84$ %, $P^{+}=99.71$ %). Finally, a double-blinded validation of the embedded algorithm was conducted on a private ePatch validation database ( $Se=99.91$ %, $P^{+}=99.79$ %). The algorithm was thus validated with high clinical performance on more than 300 ECG records from 189 different subjects with a high number of different abnormal beat morphologies. This demonstrates the strengths of the algorithm, and the potential for this embedded algorithm to improve the possibilities of early diagnosis and treatment of cardiovascular diseases.
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机译：心血管疾病预计仍将是全球死亡的唯一主要原因。这些疾病的及时诊断和治疗对于预防死亡和危险的并发症至关重要。心律失常早期诊断的重要工具之一是对从门诊长期记录中获得的心电图（ECG）进行分析。新型贴片式ECG记录器的设计增加了这些长期记录的可访问性。在许多应用中，这些设备的另一个优势是可以实时自动分析记录的ECG。因此，本研究的目的是设计一种用于自动心跳检测的新颖算法，并将该算法嵌入带有CE标志的ePatch心脏监护仪中。该算法基于新颖的计算效率级联滤波器，优化的自适应阈值处理和完善的回溯机制。该算法的设计和优化是在两个不同的数据库上进行的：MIT-BIH心律失常数据库（$ Se = 99.90 $％，$ P ^ {+} = 99.87 $）和一个私人ePatch培训数据库（$ Se = 99.88 $） ％，$ P ^ {+} = 99.37 $％）。离线验证是在欧洲ST-T数据库上执行的（$ Se = 99.84 $％，$ P ^ {+} = 99.71 $％）。最后，在私有ePatch验证数据库（$ Se = 99.91 $％，$ P ^ {+} = 99.79 $％）上对嵌入式算法进行了双盲验证。因此，该算法在来自189个具有大量不同异常搏动形态的不同受试者的300多个ECG记录上得到了高度临床验证。这证明了该算法的优势，以及该嵌入式算法改善心血管疾病早期诊断和治疗的可能性。
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