With new advances in ambulatory monitoring new challenges appear due to degradation in signal quality and limitations in hardware requirements. Existing signal analysis methods should be re-evaluated in order to adapt to the restrictive requirements of these new applications. With this motivation, we chose a robust beat detection algorithm and optimized it further to be running in an embedded platform within a cardiac monitoring sensor node. The algorithm was designed in floating point in Matlab and evaluated in order to study its performance under a wide range of conditions. The initial PC version of the algorithm obtained a good performance under a wide variety of conditions (Se=99.65% and +P=99.79% on the MIT/BIH arrhythmia database and Se=99.88%, +P=99.93% on our own database with ambulatory data). In this study, the algorithm is adapted and further optimized to work in real time on an embedded digital processor, while keeping this performance without degradation. The run-time memory usage of the application was of 150 KB with an execution time of 1.5 million cycles and an average power consumption of 494 µW for an ECG of 3 seconds length and sampling frequency of 198 Hz. The algorithm implementation in a general purpose processor will put significant limits on the performance in terms of power consumption. We propose possible specifications for an application-optimized processor for more efficient ECG analysis.
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机译：随着动态监控技术的新发展，由于信号质量下降和硬件要求受到限制，新的挑战出现了。为了适应这些新应用的严格要求，应重新评估现有的信号分析方法。出于这种动机，我们选择了一种强大的心跳检测算法，并对其进行了优化，使其可以在心脏监测传感器节点内的嵌入式平台中运行。该算法是在Matlab中的浮点设计的，并对其进行了评估，以研究其在各种条件下的性能。该算法的初始PC版本在各种条件下均具有良好的性能（在MIT / BIH心律失常数据库上，Se = 99.65％和+ P = 99.79％；在我们自己的数据库上，Se = 99.88％，+ P = 99.93％动态数据）。在这项研究中，对该算法进行了调整，并对其进行了进一步优化，以在嵌入式数字处理器上实时工作，同时保持了这种性能不变而又不降低性能。该应用程序的运行时内存使用量为150 KB，执行时间为150万个周期，ECG为3秒长，采样频率为198 Hz，平均功耗为494 µW。通用处理器中的算法实现将在功耗方面对性能造成重大限制。我们提出了针对应用程序优化的处理器的可能规格，以进行更有效的ECG分析。
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