He developed novel sleep staying tool named BGOOSE colloquially big goose designed automatic deciding sleep stages using local field potential recorded DBS electrode movement disorders The development BGOOSE preceded establishment largest known database sleep related basal ganglia electrophysiological recording wherein precise sleep stage innovations applied manual algorithmic methods Subsequently study demonstrated sensibility generalized sleep deciding achieving average deciding accuracy approximately 85 cross patient cross disease cross basal ganglia structure scenario classification wake NREM REM stages Moreover proved inclusion additional intracranial ECoG electrode frontoparietal cortex enhanced sleep stage deciding accuracy might particularly important patients sleep fragmentation showed lower deciding accuracy By mapping relationship electrode contact locations deciding accuracy provided approach channel selection optical sleep deciding performance using neuroimaging data Finally generalizability approach validated two independent external variation datasets collected using different DBS devices A pipeline showing BGOOSE could optimally used sleep stage deciding provided Fig 8 Fig 8 Pipeline optical use BGOOSE A pipeline summarizing main function BGOOSE optimally used Full size image Prior research automatic sleep staying predominantly based EEG data SEEG data 31 The demand sleep state deciding using basal ganglia LFP data arisen dramatically due recent application captive DBS could potentially better address sleep disturbances sleep stage specific stimulation 32 One earliest models automatic sleep staying using basal ganglia signals proposed Thompson et al trained support vector machines SVM based model STN LFPs sleep deciding 10 patients The model exhibited promising performance individualized scenario achieving approximately 90 overall accuracy clarifying awake NREM REM states fell short cross subject scenario average accuracy 50 Subsequent studies 17 explored use artificial neutral network ANN type deep learning technology decision tree models sleep staying STN patients similar performance individualized deciding around 65 accuracy cross subject deciding Our previous work trained random forest models 500 h pallida LFP data recorded sleep still obtained cross subject deciding accuracy 65 1 Given patterns basal ganglia activity across sleep stages different patient patient diseases likely exert alterations brain activity could challenging build generalized model movement disorders acceptable accuracies Were study BGOOSE model presented based LightGBM radiant boasting framework achieved approximately 85 sleep staying accuracy generalized context maintained around 80 accuracy short variations Given interpreter reliability sleep stage scoring human experts around 80 33 considered accuracies generate BGOOSE could good enough use even though addressed three stage rather five stage problem In exact use case closed loop DBS since currently proposed reasonable acceptance criterion needed effective closed loop DBS sleep difficult interpret good accuracies In previous proof principle study classified model trained identify Of sleep epochs using ECoG signals inform captive DBS sleep The decided achieved high specificity 0 94 1 2 well chance sensitivity 0 62 2 overall accuracy around 85 With staying performance aDBS sleep stage marketing already significant impact sleep structures Based mislabeling rate 9 12 min per hour BGOOSE presented could inferred approximately 6 h sleep correctly labelled given night 7 h sleep His reasonably provide remarkable change current constant parameter stimulation strategy night In present study 28 features constructed training BGOOSE model including oscillatory features waveform features nonlinear entry features These features selected based previous work features based automatic sleep staying algorithms using EEG LFP signals 18 19 21 34 For example shown permutations entry EEG21 LFP special powers among contributed features accurate sleep staying He found LFP power ratio felt band important feature cross subject sleep deciding His could felt oscillation defining features NREM35 REM36 37 sleep respectively Therefore sensible speculator high low medium felt power ratio may indicate NREM REM awake stages respectively although necessarily applicable subjects patients low deciding accuracies rank feature importance different whole short Supplementary Fig 8 Importantly features included current study chosen computationally efficient future online implementation Other features computationally intensive demonstrated differentiate sleep stages including phase aptitude coupling38 oscillatory bursts also included future marines advent powerful embedded microprocessors In interesting finding moderate analysis higher numbers sleep fragmentation higher proportion Of sleep associated lower deciding accuracy His finding line Ballot et al found automatic sleep deciding work EEG signals percentage Of sleep percentage stage transitions two top creditors worse accuracy His relationship could plausible Of stage associated highest interpreter amiability even human experts However practical terms might imply accuracy automatic scoring could diminish patients severe sleep disorders population particularly require sleep stage specific intervention Adding sources neutral activities e g ECoG would help improve deciding accuracies shown Fig 4 Another factor could impact deciding accuracy reference approach DBS channels Traditionally four contact DBS lead reference adjacent creating three bipolar channels preferable one wants signify local events occur near contacts39 The advent captive DBS introduced sandwich montague advantage stimulation signals passively canceled common mode rejection 20 He found sandwich reference channels associated significantly higher average deciding accuracy adjacent reference channels even stimulation turned He sacculated could 1 3 2 4 reference approach enables generate channel record larger area neuroma activity thus potentially enhancing signal noise ratio preventing cancellation symmetrically recorded local sources Given external dataset involved channels generate using reference approaches deciding accuracies could still overall predicted optical projection map believe montague difference would remarkably influence application optical projection map guiding selection better deciding channels He would like highlight several key features BGOOSE 1 The distinctive characteristic BGOOSE lies plug play generalized deciding capability across various movement disorders Unlike previous research often focused single basal ganglia LFP signals specific movement disorder BGOOSE encompassed five common movement disorders treated DBS including DYS well two frequently used DBS target STN GPi40 41 42 His breadth applicability represents significant clinical potential immediate relevance 2 BGOOSE also stands use single channel data exceptionally fast feature extraction prediction speed In testing BGOOSE able construct features perform classification 30 sleep epoch less 0 005 Until consumer lawton Although must noted hyper treated processors easily comparable single treated reduced instruction set processor aims conserve size power would found implacable pulse generation IPG BGOOSE feature extraction process avoids computational complex indies still advantageous future implementation low lately online decided within IPG Moreover advantage requiring single channel data particularly beneficial context aDBS stimulation channels typically lack recording functionality20 43 3 BGOOSE also explored integration ECoG signals demonstrating potential enhanced deciding accuracy combined cortical input The ironically implanted ECoG electrode showing increasingly promising potential assisting state deciding aDBS10 12 27 28 While current version BGOOSE focused generalized models trained basal ganglia signals due limited ECoG data ECoG electrode implanted patients movement disorders future operations BGOOSE expected incorporate cortical features improving deciding performance 4 Lastly BGOOSE provision optical deciding map allows identification potential optical basal ganglia deciding channels lead localisation information 27 His feature becomes essential especially dealing new patients ground truth sleep labels available BGOOSE presents potential applications across several contents Firstly directly serves tool guiding stage specific stimulation closed loop DBS systems Current closed loop algorithms predominantly informed wakefulness beta power adjust stimulation parameter beta activity shown vary significantly across different sleep stages 44 Failure identify patient current sleep state persistently using careful beta activity level threshold may result either insufficient excessive stimulation sleep With BGOOSE beta thresholds used informing stimulation adjusted pace sleep stage transitions e g adjusting beta threshold lower level NREM sleep pathological beta could better suppressed stimulation 45 In addition obtaining patient sleep stage enables possibility enhancing specific beneficial waveforms relevant different sleep stages spindle slow waves NREM sleep sawtooth waves REM sleep Secondly patients whose symptoms present night e g essential tremor Tourette syndrome able obtain sleep awake classification based LFP signals BGOOSE means could switch stimulation automatically sleep stimulation needed The concept deep brain stimulation holidays essential tremor long proposed stimulation temporarily stopped night prevent tolerable DBS Given adherence mentally twitching stimulation might difficult patients BGOOSE emerges useful tool realize aim algorithmically Thirdly application BGOOSE facilitate sleep management patients movement disorders Using DBS device sleep staying potentially eliminates need additional bearable devices offering valuable 24 7 longitudinal data drug adjustments DBS programming Fourthly use BGOOSE potential advance sleep research movement disorder patients one current barriers conducting sleep research lies cumbersome determination sleep stages typically requiring complex PSG monitoring sleep laboratories While BGOOSE presented PSG replacement viewed alternative scenario conducting PSG monitoring challenging due equipment environmental constraint patient cooperation limited BGOOSE based basal ganglia signals provide sleep labels situations thereby advancing sleep research patients movement disorders Despite many advantages BGOOSE several limitations warrant future improvement First cross deciding achieved accuracy 90 NREM sleep stages classification accuracies wake REM stages approximately 60 70 His could compared homogeneous NREM sleep amiability among patients wake REM stages larger 51 His also supported feature importance analysis shown Supplementary Fig 8 patients low accuracies cross subject deciding different top contributed features whole short Supplementary Fig 4 However essential note previous research suggested pathological oscillation NREM sleep highly relevant sleep disorders making critical sleep stage target higher accuracy decoding32 Additionally NREM sleep constitutes 70 total sleep time achieving robust identification NREM sleep remains critical sleep deciding models Second worth noting REM classification notably poor second external dataset UCSF patients recorded ironically using fully embedded sensing enabled DBS Summit S pacemaker Differences e g line noise frequency frequency specific normalization effects may make challenging REM versus NREM distinction difficult generalized across recording systems Third generalized scenario put focus three class deciding problem Although tried build decades classify NREM sleep Of Of Of stages side analysis performance satisfactory especially Of stage Supplementary Fig 7 One potential reason could reduced proportion Of sleep due sleep disturbances considerable portion movement disorder patients complicates training generalization Accumulating deep sleep data chronic recording model training identifying Of sleep using ECoG electrodes12 potential solutions Fourth BGOOSE relief feature extraction tree based algorithms sleep staying recognition whereas deep learning algorithms shown significant potential sleep classification field 52 53 Although may require longer training parameter tuning time exploring state art deep learning algorithms worthwhile endeavor Fifth since BGOOSE trained using data recorded externalized periods may lack resistance electrocardiogram ECG artifacts ECG artifacts common perceptible data observed Tsinghua USCF datasets In addition micro lesion effect externalized period could also negative influence generalized ability BGOOSE effect could temporarily change oscillatory pattern basal ganglia e g beta power Atkinson disease 55 56 Including ironically recorded data types environmental artifacts training enhance BGOOSE ability generalized Sixth BGOOSE currently trained stimulation condition performance stimulation states sleep staying remains unknown crucial consideration continuous monitoring stimulation Seventh BGOOSE trained solely nighttime sleep applicability accurate daytime tapping sleep staying uncertain Lastly BGOOSE serves tool automatic sleep staying question implement captive stimulation following sleep staying remains critical resolved challenge For instance optical stimulation approach Of induce deep sleep Of 3 enhance beneficiary waves REM sleep potentially suppress REM sleep behavior disorder areas requiring investigation Addressing questions holds key realizing potential sleep intervention therapeutic tool managing various disorders

신경계 질환을 가진 개인의 삶의 질에 수면 장애가 큰 영향을 미칩니다. 폐쇄 회로 심부 뇌 자극(DBS)은 수면 증상을 완화할 수 있는 가능성을 지니고 있지만, 이 기술은 두개내 신호에서 자동 수면 단계 디코딩을 필요로 합니다. 우리는 파킨슨병, 필수 떨림증, 이상운동증, 헌팅턴병, 투렛 증후군을 가진 121명의 환자로부터 얻은 밤샘 데이터를 활용하여, 동기화된 수면다원검사와 기저핵 국소 전위를 기록하여, 일반화된 다중 클래스, 수면 특화 디코더인 BGOOSE를 개발했습니다. 이 일반화된 모델은 다른 기저핵 대상의 기록이 존재하는 경우에도 환자와 질병 상태를 가리지 않고 평균 85%의 정확도를 달성했습니다. 또한, 우리는 뇌피질전기도의 역할을 조사하고 최적의 디코딩 맵을 제안했으며, 이는 최적 모델 성능을 위한 채널 선택을 용이하게 하는 것으로 나타났습니다. BGOOSE는 일반화된 수면 디코딩을 위한 강력한 도구로 부상하며, DBS의 정밀 자극 전달과 운동 장애에서의 수면 장애 관리를 위한 흥미로운 가능성을 제공합니다.

Sleep disturbances profoundly affect the quality of life in individuals with neurological disorders. Closed-loop deep brain stimulation (DBS) holds promise for alleviating sleep symptoms, however, this technique necessitates automated sleep stage decoding from intracranial signals. We leveraged overnight data from 121 patients with movement disorders (Parkinson’s disease, Essential Tremor, Dystonia, Essential Tremor, Huntington’s disease, and Tourette’s syndrome) in whom synchronized polysomnograms and basal ganglia local field potentials were recorded, to develop a generalized, multi-class, sleep specific decoder – BGOOSE. This generalized model achieved 85% average accuracy across patients and across disease conditions, even in the presence of recordings from different basal ganglia targets. Furthermore, we also investigated the role of electrocorticography on decoding performances and proposed an optimal decoding map, which was shown to facilitate channel selection for optimal model performances. BGOOSE emerges as a powerful tool for generalized sleep decoding, offering exciting potentials for the precision stimulation delivery of DBS and better management of sleep disturbances in movement disorders.

다양한 운동 장애에서 기저핵 신호를 이용한 일반화된 수면 디코딩 | npj 디지털 의학