PLMD screening and long-term treatment monitoring via mobile- connected flexible bed sensor strip Fredrik Sannholm 1 , Eliot Baker 1 , Joonas Paalasmaa 1 , Markku Partinen 2,3 1 Beddit Ltd, Espoo, Finland 2 Department of Clinical Neurosciences, University of Helsinki, Helsinki, Finland 3 Helsinki Sleep Clinic, Vitalmed Research Centre, Helsinki, Finland Introduction Health outcomes and healthcare cost savings could be substantially improved by enabling wide-spread, early identification of sleep movement disorders like Periodic Limb Movement Disorder (PLMD) via a home-based, inexpensive, and unobtrusive ballistocardiography (BCG)- based system. Measurement parameters would make apnea screening possible, as well. Materials and methods • BCG via flexible piezoelectric sensor, 70 by 3 cm, 0.2 mm thick, taped under the bed sheet at chest level. The sensor is completely unobtrusive • BCG sensor measures body movements, respiration effort, as well as heartbeats • The sensor communicates with a mobile app • A clinical study with 17 volunteers was carried out • Tibialis EMG signals and BCG signals were acquired • BCG signal was filtered using a bandpass filter (2.5 Hz - 25 Hz) to remove the effect of the respiration and heart beats • The EMG signal was analysed using an algorithm based on the AASM guidelines. The BCG signal was analysed with the same algorithm but with differing parameters. • Establishing a classification threshold was critical for the BCG analysis, as there was no differentiation between which part of the body moved, and weak limb movements weren’t always recorded Conclusion: These results suggest home screening for sleep movement disorders can be done via mobile-linked unobtrusive BCG sensors. Similar detection of central apneas appears promising as well. Significant resultant cost savings could be conferred to workplaces and health care systems, along with substantial public health benefits. Long-term treatment compliance could also be improved. The sensor also appears sensitive enough to detect sleep related breathing disorders such as obstructive and central apnea. Accuracy of PLMD detection evaluated against PLM index (PLMs/hour). PLMD = index greater than 5 Results Robust agreement between sleep clinic-identified PLM, and BCG analysis-identified PLM. Analysis method offers a promising application for apnea identification as well. Specificity = 0.83; Sensitivity = 0.91 (n=17). 1 false negative, 1 false positives out of 11 and 6 cases, respectively. PLM can be detected in one person sharing a bed with another; or in both sleepers, provided they are both using their own sensors. Comparing the PLM index (PLMs/hour), correlation = 0.88. The correlation between BCG and EMG-signal detected PLMs was 0.92 (PLM counts) and 0.86 (total leg movement counts). Heartbeats Respiration cycles The BCG sensor signal consists of heartbeats, respiration effort signal and movements A comparison of the EMG and BCG signals. The BCG signals record the limb movements as well as other body movements.

PLMD screening and long-term treatment monitoring via ...PLMD screening and long-term treatment monitoring via mobile- connected flexible bed sensor strip Fredrik Sannholm1, Eliot

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Page 1: PLMD screening and long-term treatment monitoring via ...PLMD screening and long-term treatment monitoring via mobile- connected flexible bed sensor strip Fredrik Sannholm1, Eliot

PLMD screening and long-term treatment monitoring via mobile- connected flexible bed sensor strip

Fredrik Sannholm1, Eliot Baker1, Joonas Paalasmaa1, Markku Partinen2,31Beddit Ltd, Espoo, Finland

2Department of Clinical Neurosciences, University of Helsinki, Helsinki, Finland3Helsinki Sleep Clinic, Vitalmed Research Centre, Helsinki, Finland

IntroductionHealth outcomes and healthcare cost savings could be substantially improved by enabling wide-spread, early identification of sleep movement disorders like Periodic Limb Movement Disorder (PLMD) via a home-based, inexpensive, and unobtrusive ballistocardiography (BCG)-based system. Measurement parameters would make apnea screening possible, as well.

Materials and methods•BCG via flexible piezoelectric sensor, 70 by 3 cm, 0.2 mm thick, taped under the bed sheet at chest level. The sensor is completely unobtrusive•BCG sensor measures body movements, respiration effort, as well as heartbeats•The sensor communicates with a mobile app•A clinical study with 17 volunteers was carried out•Tibialis EMG signals and BCG signals were acquired •BCG signal was filtered using a bandpass filter (2.5 Hz - 25 Hz) to remove the effect of the respiration and heart beats•The EMG signal was analysed using an algorithm based on the AASM guidelines. The BCG signal was analysed with the same algorithm but with differing parameters.•Establishing a classification threshold was critical for the BCG analysis, as there was no differentiation between which part of the body moved, and weak limb movements weren’t always recorded

Conclusion: These results suggest home screening for sleep movement disorders can be done via mobile-linked unobtrusive BCG sensors. Similar detection of central apneas appears promising as well. Significant resultant cost savings could be conferred to workplaces and health care systems, along with substantial public health benefits. Long-term treatment compliance could also be improved. The sensor also appears sensitive enough to detect sleep related breathing disorders such as obstructive and central apnea.

Accuracy of PLMD detection evaluated against PLM index (PLMs/hour). PLMD = index greater than 5

ResultsRobust agreement between sleep clinic-identified PLM, and BCG analysis-identified PLM. Analysis method offers a promising application for apnea identification as well. Specificity = 0.83; Sensitivity = 0.91 (n=17). 1 false negative, 1 false positives out of 11 and 6 cases, respectively. PLM can be detected in one person sharing a bed with another; or in both sleepers, provided they are both using their own sensors.

Comparing the PLM index (PLMs/hour), correlation = 0.88.

The correlation between BCG and EMG-signal detected PLMs was 0.92 (PLM counts) and 0.86 (total leg movement counts).

Heartbeats

Respiration cycles

The BCG sensor signal consists of heartbeats, respiration effort signal and movements

A comparison of the EMG and BCG signals. The BCG signals record the limb movements as well as other body movements.