Jenkins J, Noh K H, Guezennec A, Bump T, Arzbaecher R
Pritzker Institute of Medical Engineering, IIT Center, Chicago, IL.
Pacing Clin Electrophysiol. 1988 May;11(5):622-31. doi: 10.1111/j.1540-8159.1988.tb04558.x.
This study compares the performance of three detection algorithms for the recognition of atrial fibrillation in chronic pacing leads. Multiple serial recordings were obtained of wideband and filtered electrograms from chronic atrial and ventricular leads in dogs for a period up to 55 days following implantation. Each dog was recorded in sinus rhythm and induced atrial fibrillation. Four days were chosen for processing: The day of implantation and a day in the first, second or third, and fifth weeks. Three signal processing methods were assessed for performance in detection of atrial fibrillation: software recognition of rate with automatic threshold control, amplitude distribution, and frequency spectral analysis. A software trigger for rate determination was adjusted to thresholds of 10, 20, and 30% of maximum baseline-to-peak amplitude. At 10%, a rate boundary anywhere between 420 and 560 beats per minute (bpm) perfectly separated atrial fibrillation from sinus rhythm even though atrial electrograms were contaminated with large QRS deflections and double-sensing was present. At 20% and 30%, a rate boundary around 300 bpm could be used, but sensitivity and specificity were reduced to 90%. In amplitude distribution analysis, a percent of time within a baseline window provided perfect separation of atrial fibrillation from sinus rhythm. In all cases, the signal was within this window less than 43% of the time in atrial fibrillation, and more than 43% in sinus rhythm. In spectral analysis, frequency bands were examined for power content. In the 6 to 30 Hz band atrial fibrillation contained the greater power. Choosing 58% of total power as a discriminant, sensitivity and specificity of atrial fibrillation detection were 100% and 95% respectively.
本研究比较了三种检测算法在识别慢性起搏导线中房颤方面的性能。在犬植入慢性心房和心室导线后长达55天的时间内，获取了宽带和滤波后的心电图的多个连续记录。每只犬均在窦性心律和诱发房颤状态下进行记录。选择了四天进行处理：植入日以及第一周、第二周或第三周、第五周中的某一天。评估了三种信号处理方法在检测房颤方面的性能：具有自动阈值控制的心率软件识别、幅度分布和频谱分析。用于心率测定的软件触发阈值调整为最大基线到峰值幅度的10%、20%和30%。在10%时，每分钟420至560次心跳（bpm）之间的任何心率边界都能完美地将房颤与窦性心律区分开来，即使心房电图受到大QRS波偏转的干扰且存在双感知现象。在20%和30%时，可以使用约300 bpm的心率边界，但敏感性和特异性降至90%。在幅度分布分析中，基线窗口内的时间百分比能完美地将房颤与窦性心律区分开来。在所有情况下，房颤时信号在该窗口内的时间少于43%，窦性心律时则超过43%。在频谱分析中，检查了各频段的功率含量。在6至30 Hz频段，房颤具有更大的功率。选择总功率的58%作为判别标准，房颤检测的敏感性和特异性分别为100%和95%。
