Background: Automatic sleep stage classi\ufb01cation is essential for long-term sleep monitoring. Wearable devices show more advantages than polysomnography for home use. In this paper, we propose a novel method for sleep staging using heart rate and wrist actigraphy derived from a wearable device.<\/p>\n
Methods: The proposed method consists of two phases: multi-level feature learning and recurrent neural networks-based (RNNs) classi\ufb01cation. The feature learning phase is designed to extract low- and mid-level features. Low-level features are extracted from raw signals, capturing temporal and frequency domain properties. Midlevel features are explored based on low-level ones to learn compositions and structural information of signals. Sleep staging is a sequential problem with long-term dependencies. RNNs with bidirectional long short-term memory architectures are employed to learn temporally sequential patterns.<\/p>\n
Results: To better simulate the use of wearable devices in the daily scene, experiments were conducted with a resting group in which sleep was recorded in the resting state, and a comprehensive group in which both resting sleep and non-resting sleep were included. The proposed algorithm classi\ufb01ed \ufb01ve sleep stages (wake, non-rapid eye movement 1\u20133, and rapid eye movement) and achieved weighted precision, recall, and F1 score of 66.6%, 67.7%, and 64.0% in the resting group and 64.5%, 65.0%, and 60.5% in the comprehensive group using leaveone-out cross-validation. Various comparison experiments demonstrated the e\ufb00ectiveness of the algorithm. Conclusions: Our method is e\ufb03cient and e\ufb00ective in scoring sleep stages. It is suitable to be applied to wearable devices for monitoring sleep at home.<\/p>\n","protected":false},"excerpt":{"rendered":"
Background: Automatic sleep stage classi\ufb01cation is essential for long-term sleep monitoring. Wearable devices show more advantages than polysomnography for home use. In this paper, we propose a novel method for sleep staging using heart rate and wrist actigraphy derived from a wearable device. Methods: The proposed method consists of two phases: multi-level feature learning and […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"Computers in Biology and Medicine","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2018-10-10","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":0,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13553],"msr-publication-type":[193715],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-587953","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-medical-health-genomics","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2018-10-10","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"Computers in Biology and Medicine","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-content\/uploads\/2019\/05\/2018SCICBMSleep-stage-classification-based-on-multi-level-feature-learning-and-recurrent-neural-networks-via-wearable-device.pdf","id":"587956","title":"2018scicbmsleep-stage-classification-based-on-multi-level-feature-learning-and-recurrent-neural-networks-via-wearable-device","label_id":"243109","label":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[{"id":587956,"url":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-content\/uploads\/2019\/05\/2018SCICBMSleep-stage-classification-based-on-multi-level-feature-learning-and-recurrent-neural-networks-via-wearable-device.pdf"}],"msr-author-ordering":[{"type":"text","value":"Xin Zhang","user_id":0,"rest_url":false},{"type":"text","value":"Weixuan Kou","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Eric Chang","user_id":31709,"rest_url":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Eric Chang"},{"type":"text","value":"He Gao","user_id":0,"rest_url":false},{"type":"text","value":"Yubo Fan","user_id":0,"rest_url":false},{"type":"text","value":"Yan Xu","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[199560],"msr_event":[],"msr_group":[],"msr_project":[170702],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"article","related_content":{"projects":[{"ID":170702,"post_title":"eHuatuo: Teaching Computer to Read Medical Records","post_name":"ehuatuo-teaching-computer-to-read-medical-records","post_type":"msr-project","post_date":"2011-04-10 20:16:13","post_modified":"2019-05-16 04:27:03","post_status":"publish","permalink":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/project\/ehuatuo-teaching-computer-to-read-medical-records\/","post_excerpt":"eHuatuo is an eHealthcare project about Teaching Computer to Read Medical Records developed by Microsoft Research Asia. 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