There have been a number of articles published in support of MMT’s and its partner’s scientific approach. In particular emphasis on the need for adequate and continuous testing so that the risks posed by strokes can be mitigated.

Validation of a New Heart Rate Measurement Algorithm

This study by MMT’s partner Preventicus investigates the accuracy of a heart rate (HR) measurement algorithm applied to a pulse wave. The results of the HR measured by pulse curves were extremely consistent (R > 0.99) with the HR measured on ECGs. For most standard linear HRV parameters as well, high correlations of R ‡ 0.90 in the analysis were achieved in the time and frequency domain.

Smart detection of atrial fibrillation

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice, and its paroxysmal nature makes its detection challenging. Preventicus recorded 5 min video files with the pulse wave extracted from the green light spectrum of the signal. RR intervals were automatically identified. For discrimination between AF and SR, we tested three different statistical methods. For discrimination between AF and SR, ShE yielded the highest sensitivity and specificity with 85 and 95%, respectively. Applying a tachogram filter resulted in an improved sensitivity of 87.5%, when combining ShE and nRMSSD, while specificity remained stable at 95%. A combination of SD1/SD2 index and nRMSSD led to further improvement and resulted in a sensitivity and specificity of 95%.

Validation of Photoplethysmography-Based Sleep Staging Compared With Polysomnography

MMT’s sleep analysis is powered by Philips Wearable Sensing algorithms. This clinical trial is using wrist-worn PPG to analyze heart rate variability and an accelerometer to measure body movements, sleep stages and sleep statistics were automatically computed from overnight recordings. Sleep–wake, 4-class (wake/N1 + N2/N3/REM) and 3-class (wake/NREM/REM) classifiers were trained on 135 simultaneously recorded PSG and PPG recordings of 101 healthy participants and validated on 80 recordings of 51 healthy middle-aged adults. The sleep–wake classifier obtained an epoch-by-epoch Cohen’s κ between PPG and PSG sleep stages of 0.55 ± 0.14, sensitivity to wake of 58.2 ± 17.3%, and accuracy of 91.5 ± 5.1%. κ and sensitivity were significantly higher than with actigraphy (0.40 ± 0.15 and 45.5 ± 19.3%, respectively).