Jose Maria Perez-Macias

Tampere, Finland

Ph.D. candidate in health data analysis. Health Scientist with proven track of publications, recognized in the Nokia 2017 Awards and won 3 full year grants in 2016, 2017, and 2018 by the Suomalainen Tiede Akatemia. I dig into health data, publish outcomes, build data analysis frameworks. Authored and co-authored several journals and participated in extra-curricular group activities such as debate and improv theater. Expert in sleep studies, wearables, and applied health technologies. I use advanced algorithms, signal processing, and data science (statistical analyses, machine learning, and deep learning/neural networks) on time series (sound and audio processing, Ballistocardiography, Accelerometers, and EEGs) and images to derive non-apparent physiological signals, to detect relevant events such as snoring or sleep positions, or complex diagnosis such as specific breathing disorders like abnormal heart beats, sleep apnea, or asthma. My thesis verses on state-of-the-art algorithms for


  • Spectral analysis of snoring events from an Emfit mattress
  • Detection of Snores Using Source Separation on an Emfit Signal
  • Time characteristics of prolonged partial obstruction periods using an Emfit mattress
  • Detection and assessment of sleep-disordered breathing with emfit mattress
  • Assessment of support vector machines and convolutional neural networks to detect snoring using Emfit mattress
  • Comparative assessment of sleep quality estimates using home monitoring technology
  • The use of crowdsourcing for dietary self-monitoring: Crowdsourced ratings of food pictures are comparable to ratings by trained observers
  • Spectral changes in spontaneous MEG activity across the lifespan
  • Sleep Monitoring Data Validation using a Linear Discriminant Classifier
  • The use of wrist EMG increases the PPG Heart Rate accuracy in smartwatches
  • Methods for Detecting Abnormal Ventilation in Children - the Case Study of 13-Years old Pitt-Hopkins Girl
  • Heart pulse demodulation from Emfit mattress sensor using spectral and source separation techniques.

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