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首页> 外文期刊>PACE: Pacing and clinical electrophysiology >Feasibility of automated detection of advanced sleep disordered breathing utilizing an implantable pacemaker ventilation sensor.
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Feasibility of automated detection of advanced sleep disordered breathing utilizing an implantable pacemaker ventilation sensor.

机译:利用植入式起搏器通气传感器自动检测高级睡眠障碍性呼吸的可行性。

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OBJECTIVES: This study tested the feasibility of automatically detecting advanced sleep disordered breathing (SDB) from a pacemaker trans-thoracic impedance sensor. BACKGROUND: SDB is prevalent yet under-diagnosed in patients with cardiovascular disease. The potential for automated detection of SDB in patients receiving pacemakers with respiration sensors has not been fully explored. We hypothesized that the trans-thoracic impedance sensor could be utilized for automatic detection of advanced SDB. METHODS: Patients underwent overnight polysomnography (PSG). The pacemaker trans-thoracic impedance signal was simultaneously recorded and time synchronized with the polysomnograph. Cardiovascular health variables were abstracted from medical records. Apnea was defined as cessation of inspiratory airflow lasting 10 seconds or longer. Hypopnea was defined as a reduction of tidal volume of at least 30% from baseline tidal volume, lasting 10 seconds or more. A computer algorithm (PM-A) was developed to automatically detect SDB from the pacemaker impedance sensor data. The performance of automated SDB detection was compared against PSG. RESULTS: Sixty patients (aged 69 +/- 12 years, 45 males) were studied. Advanced SDB (moderate or severe) was diagnosed in 40 patients. Severe SDB (apnea-hypopnea index [AHI]> or = 30) was diagnosed in 32 patients (53%), but only 5 patients had prior diagnosis of the disease. Moderate SDB (30 > AHI > 15) was diagnosed in 8 patients of whom only two were previously diagnosed. Cardiovascular health variables did not predict the presence of advanced SDB. PM-A derived AHI correlated with that of the PSG (r = 0.80, P < 0.01). The algorithm identified patients with advanced SDB with 82% sensitivity and 88% specificity. CONCLUSIONS: It is feasible to automatically measure SDB severity using a pacemaker trans-thoracic impedance sensor. Advanced SDB was frequently undiagnosed in this cohort of pacemaker patients.
机译:目的:本研究测试了从起搏器经胸阻抗传感器自动检测晚期睡眠呼吸障碍(SDB)的可行性。背景:SDB在心血管疾病患者中普遍存在,但诊断不足。尚未充分探索在接受带呼吸传感器的起搏器的患者中自动检测SDB的潜力。我们假设跨胸阻抗传感器可以用于高级SDB的自动检测。方法:患者接受过夜多导睡眠监测(PSG)。同时记录心脏起搏器的经胸阻抗信号,并与多导睡眠图同步时间。从医疗记录中提取心血管健康变量。呼吸暂停被定义为持续10秒或更长时间的吸气气流停止。呼吸不足定义为潮气量比基线潮气量减少至少30%,持续10秒或更长时间。开发了一种计算机算法(PM-A),以从起搏器阻抗传感器数据中自动检测SDB。将自动SDB检测的性能与PSG进行了比较。结果:研究了60例患者(69 +/- 12岁,男性45例)。 40例患者被诊断为晚期SDB(中度或重度)。在32例患者(53%)中诊断出严重的SDB(呼吸暂停低通气指数[AHI]>或= 30),但只有5例患者事先诊断出该病。在8例患者中诊断为中度SDB(30> AHI> 15),先前仅诊断出2例。心血管健康变量不能预测晚期SDB的存在。 PM-A衍生的AHI与PSG的相关(r = 0.80,P <0.01)。该算法以82%的敏感性和88%的特异性鉴定出患有晚期SDB的患者。结论:使用起搏器经胸阻抗传感器自动测量SDB的严重性是可行的。在这个起搏器患者队列中,常常无法诊断出晚期SDB。

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