CESAER Annual Seminar: Biomedical Signal Processing in Vital Sign Analysis: a Key Role for the Implementation of Advanced Technological Tools

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1 CESAER Annual Seminar: Healthy Living, Healthy Ageing a Challenge for Medicine AND Engineering, October 8th, 2105 Biomedical Signal Processing in Vital Sign Analysis: a Key Role for the Implementation of Advanced Technological Tools Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Italy

2 Biological signals carry information about the physiological systems under studying. The processing of signals allow: i) to quantify and ii) to qualify such information (for validating physiological modelling) mainly through mathematical tools

3 Wearable Devices and the Society of Information

4 Wearable system It detects and monitors data and signals with an improved comfort degree for the user in respect to portable monitoring systems Goal: to realize non-intrusive systems which do not interfere with the daily activities of monitored subject Burden/ Weight Portable devices Wearable devices Devices Comfort/Daily Activity

5 Wearable vs portable

6 General structure of a wearable system Sensors system Integrated or not into the fabric in the cloth/accessory (textile or non-textile electrodes or sensors) Applied to the cloth (i.e. accellerometers: not all sensors are completly wearable) Wearable electrode connections & pre-processing General purpose processing Amplification Filtering A/D convertion SNR improvement Basic parameter extraction Transmission User interface (iphone, Tablet, PDA, PC, etc) Dedicated processing Parameter extraction (real time or off-line) Classification Feedback message generation (alarms, graphic record, etc.)

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8 ECG monitoring Contemporaneous acquisition of 5 ECG leads: Pseudo Einthoven Leads: I, II, III Precordial leads: V2, V5

9 Measurement of respiration through piezoresistive electrodes Thoracic and abdominal respiration signals

10 TakeCare: Risk management modules Focus home Sleep quality improvement Stress management Daily activity management Weight management

11 Relaxation tool 1 example 1: on line measure of the stress level 11

12 Wearable / textile sensors for vital signals acquisition CSEM Resp (25 Hz, 1/beat) BA BR ECG (250 Hz) HR, HRV Smartex

13 Sleep Signal Acquisition Bed Foil (VTT) Bed Sensor with 8 channel piezo foils

14 SLEEP STUDIES

15 Bodily accellerometer

16 Sleep fragmentation index Sleep Fragmentation Index (SFI) SFI = 3* (No. Arousals in TST 1/3) + (No. Arousals in TST 2/3) * (No. Arousals in TST 3/3). SFI < < SFI < 100 SFI > 100 GOOD MODERATE BAD Sensitivity = 81% Specificity = 99% Accuracy = 98.5%

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18 Application: behavioural therapy for insomnia HRV/Res Activity Sleep quality Caffein Stress h sleep h wake-up...

19 Classification stage Mean RR VLF power HF power Parameters ANN Classificator HMM Feature # 2 v1 v 2 v 3 v 4 Sleep profile Apnee KNN Feature # 1 b21 b 22 b 23 b 24 a 22 wake a 21 a 12 a 32 a 23 a 31 a 11 NREM REM a 13 a 13 b 11 b 12 b 13 b 14 b 31 b 32 b 33 b 34 v 1 v 2 v 3 v 4 v 1 v 2 v 3 v 4

20 Results 7 Beat-to-beat features Mean over 30 sec Classificator HMM (REM/nonREM/awake) 24 subjects HSE (San Raffaele Hospital Sleep Centre, Milano): Accuracy 80%, SP 85%, SE 71% MO Mendez et al.,. Int J Biom Engin & Tech, 2010 Automatic classification (R&K) Mean accuracy (70-90% in normal subjects, 65-87% in sleep disturbances Mean agreement among different examiners (87,5%)

21 PSYCHOPHYSICAL STRESS

22 Relaxation %RSA

23 Relaxation tool: on line feedback during relaxation exercise 23

24 HeartCycle (HF and CAD) ( )

25 Overall scheme Triax Features extraction Acceleration data Barometric pressure data Features extraction HMM classifier GMM classifier BLACK BOX MODEL Oxygen consumption

26 Energy expenditure (black-box O2 consumption) With classification information Signal magnitude and Pressure Gradient are the model input. Oxygen Uptake (mlo 2 /(Kg*min)) is the model output. Without classification information The classification information is used to improve the model performance. Energy expenditure estimation during normal ambulation using triaxial accelerometry and barometric pressure., Wang J. et al., Physiol Meas Nov;33(11):

27 Summary Camera Pulsometry Apps for which FDA intends to exercise enforcement discretion Heart rate derived from video images For entertainment or fitness use only Smartphone as ECG monitor Apps that transform a mobile platform into a regulated medical device, regulated by FDA. ECG acquired by external devices (case, electrodes) connected to the phone For medical use More validation studies, clinical utility and feasibility proved (AF) Available by prescription or over-the-counter Few validation studies, accuracy and reproducibility tbd Available through appstores

28 Signal processing connected to wearable devices The recorded signals do not generally respect the clinical standards It is not always possible to detect a signal which is considered standard for a given application Lower quality of the signals in respect to more dedicated devices Presence of noise due to posture and movements Need of had-hoc methods of biosignal processing

29 Conclusions Advancements in mobile health (mhealth) ECG technology greatly empowers and enables the possibility for unprecedented patient participation and active engagement in their own medical education and healthcare. Proper information is needed to avoid misuse of health applications not designed for medical purposes (i.e., for fitness). This includes proper validation and knowledge of limits of operability. Anytime ECG monitoring by FDA-approved smartphone medical use is intuitive and has the potential to allow users to learn about and characterize their heart rates & rhythms, as well as to provide global identification and early diagnosis of arrhythmias at any time. Atrial Fibrillation seems to represent one important field of application for this technology, with benefits both in community AF screening and detection of AF recurrence. Caiani EG, ESC 2014