Beigl, M.

Budde, M.

Köpke, M.

Röddiger, T.

English

KITopen

VOCNEA: Sleep Apnea and Hypopnea Detection Using aNovel Tiny Gas SensorTobias Röddiger, Michael Beigl, Marcel Köpke, Matthias BuddeTECO / Pervasive Computing Systems, Karlsruhe Institute of Technology, Karlsruhe, Germany{roeddiger, michael, koepke, budde}@teco.eduABSTRACTSleep breathing disorder is a serious threat to a large share ofthe population. This paper presents a low-cost, tiny sensorsystem based on Volatile Organic Component (VOC) sensingfor the detection of sleep apnea/hypopnea. We present twodesigns, discuss wearability aspects and show that the sensorworks similar to gold standard Polysomnography (PSG).ACM Classification KeywordsJ.3 Life and Medical Sciences: Medical information systems;H.5.m Information Interfaces and Presentation (e.g., HCI):MiscellaneousAuthor KeywordsWearable; Apnea; Hypopnea; Information System; HealthINTRODUCTION AND RELATED WORKPeople with sleep apnea repeatedly stop breathing for 10 sec intheir sleep. A significant share of the population (24% of men,9% of woman in the US [2]) is affected. Obstructive SleepApnea (OSA) is diagnosed if apnea (breathing suspension)and hypopnea (breathing reduction) occur more than 5 (mildapnea), 15 (moderate) or 30 (severe) times per hour. Thegold standard for detection is Polysomnography (PSG), wherenasal and oral airflow is measured with pressure transducers(Fig. 1). In sleep laboratories, additional data is recorded(e.g. EEG, EOG, EMG, ECG, pulse oximetry, audio, video),which allows medical practitioners to diagnose the type ofsleep disorder. To simply detect breathing apnea/hypopnea,airflow measurements suffice.Several approaches to detect sleep apnea have been published,using blood oxygen level sensing, mobile ECG, thermistorsattached to the nose, wave reflection changes (e.g. sonar,radar, depth video), change of chest volume, Peripheral Ar-terial Tonometry and combinations thereof. Mobile devices(e.g. Somnapatch) measure only nasal respiration. Accu-racy ranges from 50-98%, depending on factors such as usedequipment (from smart watches to medical devices) and eval-uation methodology (see [3], [1], [5]). These approaches areeither uncomfortable (e.g. PSG), non PSG-like precision (e.g.Smartwatch, chest belt (moving while sleep), sensor mat) orhave restrictions regarding use (e.g. sonar, radar with multiplepersons in a room).VOCNEA DEVICE WORKING PRINCIPLEIn contrast to previous work, our goal was to measure exha-lation of air directly and not via an indirect indicator. This issimilar to the airflow measurements in gold standard PSG. Incontrast to PSG, our design goal was a tiny and cheap devicethat provides an indication for a patient using a simple-to-use system, but not to develop a replacement for sleep labexamination. Still, it should be capable of calculating theApnea-Hypopnea Index (AHI) precisely without requiring anasal cannula or face-covering mask.VOCNEA (VOC Apnea and Hypopnea Detection) is imple-mented as a tiny ”electronic” patch. The measurement princi-ple is based on VOC (Volatile Organic Compound) gas sensing.During exhalation, the VOC concentration constantly rises,whereas it immediately drops while inhaling when gases mixwith the surrounding air. The VOC background in ambient airis normally below 10 ppb and will not rise over 1 ppm even inhighly polluted indoor environments (e.g. due to toxic furni-ture or painting exposure) [4]. Human breath contains ~2 ppmVOC (ammonia, methanol, ethanol and other volatile organiccompounds) due to biological processes. Thus, sensing thedifference between breath and background VOC is a clear andsimple method for supervising human breathing.Figure 1. Left: A sleep lab setting with PSG airflow measurementand other sensors (image CC BY-SA 3.0 Wikimedia Commons/Halicki).Right: Potential attachment points for the VOCNEA device.DESIGN AND WEARABILITYAccording to wearability constraints [6], there are two possiblepositions for the sensor: around the chest or mouth/nose. ForVOC only the area around the nose/mouth is useful. Weightand size constraints for this area [6] restrict a device to lessthan 200g and much less than 20mm. We started by designingfour options for attachment: Nasal ridge (right, left), nasalwing or philtrum (Fig. 4). Electronics are integrated into apatch of tape (Fig. 3). The sticky side of the patch is attachedto the skin at 4 possible positions as depicted in Fig. 1 (right).Positions were tested in a small study with 8 subjects on thestreet (random sample of convenience; 50% female, age 21-54(mean 32), four eyeglasses wearer). They had to stick thepatch mockup to given locations and rate the relative comfortas rank (1,2,3) and overall comfort (very comfortable to veryuncomfortable). Candidates show a clear preference for nasalwing followed by nasal ridge, rating them as comfortable orvery comfortable to wear (Fig. 2). The design consequencewas that only the tiny VOC sensor is attached on the mostsensitive philtrum with a cable/flex board connecting to thebattery and main electronics on the nasal wings.Figure 2. Wearability comfort by positionVOCNEA DEVICE TECHNICAL DESIGNWe conducted a 2-step prototyping process, with a proof-of-concept phase in between: In the first phase, off-the-shelfmodules were used, and after design adaption, an integratedflex-board for unobtrusive integration into a standard patchwas assembled. Technical basis is the Bosch BME680 ultra-low power gas sensor module (12mA with 2.5 Hz samplingfreq.). It contains a VOC, temp. and humidity sensor (forcalibration), analog and digital circuitry in a tiny package(3x3x1mm) and can detect VOC reliably with high precision(~0.02ppm).Figure 3. first (left) and second (right) phase prototype.The overall system consists of a BLENano2 module for send-ing the measurement to a smartphone that calculates AHI. anda LiPo rechargeable battery. Power management is done bythe BLE nano module. After proof of concept, a novel devicehas been designed based on the same electrical componentsbut using an integrated 0.3mm thick Flex PCB (Fig. 3 right) asthe basis for electronics and the connection between the sen-sor, battery, and electronics. The 2nd prototype weighs 2.1g(<1mg at philtrum) excluding patch. The size is 40x50x5mmoutline. Electronics are PU-coated (1µm) for environmentalprotection. A single piece BOM is 20C(BME680:8C, BLE-module nRF52832: 9C, LIR2450: 3C) plus FlexPCB. Thetime of battery operation is >8h.Figure 4. Graph for sleep disorder detection (x-axis: time [s], y-axis:VOC [ppb]). Apnea event marked blue, hypopnea yellow.RESULTS AND OUTLOOKFig. 4 shows a typical sleep disorder detection. Since breath-ing is a periodical process, we employed a spectral analysison moving time intervals of 30s. The spectrum shows a clearbreathing peak with a period of 5-8s. Apnea is detected if thispeak does not occur. Hypopnea is detected if it gets blurred orits amplitude falls below a given (relative) threshold. In gen-eral, this method allows for on-the-fly calibration via anomalydetection. Differences between mouth/nose breathing are notsignificant. As expected, our tests with 2 subjects yielded a100% detection rate of apnea and hypopnea events. Futuredesigns and tests will be done by a lab certified for medical-grade devices, including a long-term comparison study withgold standard PSG.REFERENCES1. Joachim Behar, Aoife Roebuck, Joao S Domingos, ElnazGederi, and Gari D Clifford. 2013. A review of currentsleep screening applications for smartphones.Physiological measurement 34, 7 (2013), R29.2. Fred Ferri. 2014. Ferri’s Clinical Advisor 2015: 5 booksin one. Elsevier Health Sciences, 1090.3. Rajalakshmi Nandakumar, Shyamnath Gollakota, andNathaniel Watson. 2015. Contactless sleep apneadetection on smartphones. In MobiSys’17. ACM, 45–57.4. US EPA. 2011. Background Indoor Air Concentrations ofVolatile Organic Compounds in North AmericanResidences (1990–2005). Office of Solid Waste andEmergency Response Washington, DC.5. Cheng Yang, Gene Cheung, Vladimir Stankovic, KevinChan, and Nobutaka Ono. 2017. Sleep apnea detectionvia depth video and audio feature learning. IEEETransactions on Multimedia 19, 4 (2017), 822–835.6. Clint Zeagler. 2017. Where to wear it. In ISWC ’17.ACM, 150–157.      Repository KITopen  Dies ist ein Postprint/begutachtetes Manuskript.  Empfohlene Zitierung:  Röddiger, T.; Beigl, M.; Köpke, M.; Budde, M. VOCNEA: Sleep apnea and hypopnea detection using a novel tiny gas sensor. 2018. 22nd International Symposium on Wearable Computers, ISWC 2018; Singapore; Singapore; 8 October 2018 through 12 October 2018, ACM, New York (NY).  doi: 10.5445/IR/1000088393    Zitierung der Originalveröffentlichung:  Röddiger, T.; Beigl, M.; Köpke, M.; Budde, M. VOCNEA: Sleep apnea and hypopnea detection using a novel tiny gas sensor. 2018. 22nd International Symposium on Wearable Computers, ISWC 2018; Singapore; Singapore; 8 October 2018 through 12 October 2018, 226–227, ACM, New York (NY).  doi:10.1145/3267242.3267273                Lizenzinformationen: KITopen-Lizenz 

VOCNEA: Sleep apnea and hypopnea detection using a novel tiny gas sensor

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VOCNEA: Sleep apnea and hypopnea detection using a novel tiny gas sensor

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