There have been a number of articles published in support of Corsano’s and its partners’ scientific approach. In particular, the research underscores the need for adequate and continuous testing so that the risks posed by strokes can be mitigated.


The accuracy of heartbeat detection using photoplethysmography technology in cardiac patients

This validation study showed that the Corsano 287 CardioWatch/Bracelet with PPG-technology can determine HR and RR-intervals with high accuracy in a cardiovascular patient population, with high quality output in different subgroups, especially when combined with a signal quality indicator. Due to their non-intrusive and convenient nature, wearable devices like these have great potential for high volume accessible long-term monitoring at-risk cardiac patients.


Continuous respiration rate monitoring using photoplethysmography technology in patients with Obstructive Sleep Apnea (Abstract)

Respiration rate is an important physiological parameter whose abnormality has been regarded as an important indicator of various serious illnesses. Photoplethysmography (PPG) in wearable sensors potentially plays an important role in early disease detection by making respiration rate measurements more accessible. We investigated the accuracy of a new non-invasive, continuous, wrist-worn and wireless monitoring PPG device (Corsano CardioWatch 287) in measuring respiration rate (RR) and pulse rate (PR) at rest.

J.M. Gehring 1, E.J. Japenga 1,2, L.C. Saeijs-van Niel 1, L.P ten Bosch-Paniagua 1, M.H. Frank 1,3

1. Department of Sleep Medicine, Haaglanden Clinics, Nieuwe Parklaan 11, The Hague, The Netherlands
2. Department of Respiratory Medicine, Haaglanden Medisch Centrum, Lijnbaan 32, The Hague, The Netherlands
3. Department of Oral Surgery, Haaglanden Clinics, Nieuwe Parklaan 11, The Hague, The Netherlands

Corsano Health Cardiowatch 287 USA Usability Study

Regulations and standards put the emphasis on usability and human factor design in the development of medical device and risk management. This paper explains the main challenges for wearable devices by showing how usability and human factors study was performed on the Corsano Cardiowatch 287 bracelet for body metrics monitoring.

Validation of a New Heart Rate Measurement Algorithm

This study by Corsano’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%.

Smart detection of atrial fibrillation.pdf, Lian Krivoshei, Stefan Weber, Thilo Burkard, Anna Maseli, Noe Brasier, Michael Ku ̈hne, David Conen, Thomas Huebner and Andrea Seeck

Validation of Photoplethysmography-Based Sleep Staging Compared With Polysomnography


Corsano’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).

Fonseca et al. – 2017 – Validation of Photoplethysmography-Based Sleep Staging Compared With Polysomnography in Healthy Middle Aged Adul.pdf, Pedro Fonseca, MSc, Tim Weysen, MSc, Maaike S. Goelema, MSc, Els I.S. Møst, PhD, Mustafa Radha, MSc, Charlotte Lunsingh Scheurleer, MSc, Leonie van den Heuvel, MSc, Ronald M. Aarts, PhD