Automatic Detection of Hot Flash Occurrence and Timing from Skin Conductance Activity

A novel Hot-Flash classification algorithm via multi-sensor features integration

We aim to evaluate the feasibility and performance of a novel hot flash (HF) classification algorithm based on multisensor features integration using commercial wearable sensors. First, we processed feature sets from wrist-based multi-sensor data (photoplethysmography, motion, temperature, skin conductance and). Then, we classified (Decision Tree) physiological-recorded HFs (N=27) recorded from three menopause women, and we assessed the algorithm performance against gold-standard HF expert evaluation. The results indicated that while skin conductance features alone explain most of the variance (~65%) in HF classification, the multi-sensor approach achieved above 90% sensitivity at 95.6% specificity in HF classification and showed advantages under conditions of signal corruption and different biobehavioral states (sleep vs wake). The proposed new multi-sensor approach showed being promising in HF classification using common commercially-available wearable sensors and target locations.Clinical Relevance- The development of "user-centered" accurate, automatic detection systems for HFs can advance the measurement and treatment of HFs.

Changes in heart rate and blood pressure during nocturnal hot flashes associated with and without awakenings

Hot flashes (HFs) are a hallmark of menopause in midlife women. They are beyond bothersome symptoms, having a profound impact on quality of life and wellbeing, and are a potential marker of cardiovascular (CV) disease risk. Here, we investigated the impact on CV functioning of single nocturnal HFs, considering whether or not they were accompanied by arousals or awakenings. We investigated changes in heart rate (HR, 542 HFs), blood pressure (BP, 261 HFs), and pre-ejection period (PEP, 168 HFs) across individual nocturnal physiological HF events in women in the menopausal transition or post-menopause (age: 50.7 ± 3.6 years) (n = 86 for HR, 45 for BP, 27 for PEP). HFs associated with arousals/awakenings (51.1%), were accompanied by an increase in systolic (SBP; ~6 mmHg) and diastolic (DBP; ~5 mmHg) BP and HR (~20% increase), sustained for several minutes. In contrast, HFs occurring in undisturbed sleep (28.6%) were accompanied by a drop in SBP and a marginal increase in HR, likely components of the heat dissipation response. All HFs were accompanied by decreased PEP, suggesting increased cardiac sympathetic activity, with a prolonged increase for HFs associated with sleep disruption. Older age predicted greater likelihood of HF-related sleep disturbance. HFs were less likely to wake a woman in rapid-eye-movement and slow-wave sleep. Findings show that HFs associated with sleep disruption, which are in the majority and more likely in older women, lead to increases in HR and BP, which could have long-term impact on nocturnal CV restoration in women with multiple HFs.

Automatic Artifact Detection in Impedance Cardiogram Using Pulse Similarity Index

Impedance cardiography (ICG) is a noninvasive technique for evaluation of cardiac hemodynamic parameters such as cardiac output and pre-ejection period. However, the sensitivity of the technique to motion artifact, electrode displacement, and cardiovascular pathologies can severely impact the accuracy of hemodynamic parameter estimates. In this paper, we proposed a new algorithm for the automatic detection and exclusion of corrupted ICG cardiac cycles by defining a pulse similarity index that quantifies the level of pulse corruption and its diversion from a typical-shaped pulse. The index considers different features (activity, structure, shape, and pattern) of the ICG cardiac cycles. The algorithm is compared on sleep data collected from 20 participants against expert identified corrupted cycles. The artifact rejection algorithm achieved a high accuracy of 96% in detection of expert-identified corrupted ICG cycles, including those with normal amplitude as well as out-of-range values, and was robust to different types and levels of artifact. The algorithm shows promise toward applications requiring accurate and reliable automatic measurement of cardiac hemodynamic parameters from prolonged data sets.