Siamese Sleep Transformer For Robust Sleep Stage Scoring With Self-knowledge Distillation and Selective Batch Sampling

In this paper, we propose a Siamese sleep transformer (SST) that effectively extracts features from single-channel raw electroencephalogram signals for robust sleep stage scoring. Despite the significant advances in sleep stage scoring in the last few years, most of them mainly focused on the increment of model performance. However, other problems still exist: the bias of labels in datasets and the instability of model performance by repetitive training. To alleviate these problems, we propose the SST, a novel sleep stage scoring model with a selective batch sampling strategy and self-knowledge distillation. To evaluate how robust the model was to the bias of labels, we used different datasets for training and testing: the sleep heart health study and the Sleep-EDF datasets. In this condition, the SST showed competitive performance in sleep stage scoring. In addition, we demonstrated the effectiveness of the selective batch sampling strategy with a reduction of the standard deviation of performance by repetitive training. These results could show that SST extracted effective learning features against the bias of labels in datasets, and the selective batch sampling strategy worked for the model robustness in training.Comment: Submitted to 2023 11th IEEE International Winter Conference on Brain-Computer Interfac

脳波信号解析に注目したノイズ除去、特徴抽出、実験観測応用を最適化する数理基盤に関する研究

Electroencephalography (EEG) data inevitably contains a large amount of noise particularly from ocular potentials in tasks with eye-movements and eye-blink, known as electrooculography (EOG) artifact, which has been a crucial issue in the braincomputer- interface (BCI) study. The eye-movements and eye-blinks have different time-frequency properties mixing together in EEGs of interest. This time-frequency characteristic has been substantially dealt with past proposed denoising algorithms relying on the consistent assumption based on the single noise component model. However, the traditional model is not simply applicable for biomedical signals consist of multiple signal components, such as weak EEG signals easily recognized as a noise because of the signal amplitude with respect to the EOG signal. In consideration of the realistic signal contamination, we newly designed the EEG-EOG signal contamination model for quantitative validations of the artifact removal from EEGs, and then proposed the two-stage wavelet shrinkage method with the undecimated wavelet decomposition (UDWT), which is suitable for the signal structure. The features of EEG-EOG signal has been extracted with existing decomposition methods known as Principal Component Analysis (PCA), Independent Component Analysis (ICA) based on a consistent assumption of the orthogonality of signal vectors or statistical independence of signal components. In the viewpoint of the signal morphology such as spiking, waves and signal pattern transitions, A systematic decomposition method is proposed to identify the type of signal components or morphology on the basis of sparsity in time-frequency domain. Morphological Component Analysis (MCA) is extended the traditional concept of signal decomposition including Fourier and wavelet transforms and provided a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases being independent of each other and uniqueness representation, called the concept of “dictionary”. MCA is applied to decompose the real EEG signal and clarified the best combination of dictionaries for the purpose. In this proposed semi-realistic biological signal analysis, target EEG data was prepared as mixture signals of artificial eye movements and blinks and iEEG recorded from electrodes embedded into the brain intracranially and then those signals were successfully decomposed into original types by a linear expansion of waveforms such as redundant transforms: UDWT, DCT,LDCT, DST and DIRAC. The result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST and DIRAC to represent the baseline envelop, multi frequency wave forms and spiking activities individually as representative types of EEG morphologies. MCA proposed method is used in negative-going Bereitschaftspotential (BP). It is associated with the preparation and execution of voluntary movement. Thus far, the BP for simple movements involving either the upper or lower body segment has been studied. However, the BP has not yet been recorded during sit-to-stand movements, which use the upper and lower body segments. Electroencephalograms were recorded during movement. To detect the movement of the upper body segment, a gyro sensor was placed on the back, and to detect the movement of the lower body segment, an electromyogram (EMG) electrode was placed on the surface of the hamstrings and quadriceps. Our study revealed that a negative-going BP was evoked around -3 to -2 seconds before the onset of the upper body movement in the sit-to-stand movement in response to the start cue. The BP had a negative peak before the onset of the movement. The potential was followed by premotor positivity, a motor-related potential, and a reafferent potential. The BP for the sit-to-stand movement had a steeper negative slope (-0.8 to -0.001 seconds) just before the onset of the upper body movement. The slope correlated with the gyro peak and the max amplitude of hamstrings EMG. A BP negative peak value was correlated with the max amplitude of the hamstring EMG. These results suggested that the observed BP is involved in the preparation/execution for a sit-to-stand movement using the upper and lower body. In summary, this thesis is help to pave the practical approach of real time analysis of desired EEG signal of interest toward the implementation of rehabilitation device which may be used for motor disabled people. We also pointed out the EEG-EOG contamination model that helps in removal of the artifacts and explicit dictionaries are representing the EEG morphologies.九州工業大学博士学位論文 学位記番号:生工博甲第290号 学位授与年月日:平成29年3月24日1 Introduction|2 Research Background and Preliminaries|3 Introduction of Morphological Component Analysis|4 Two-Stage Undecimated Wavelet Shrinkage Method|5 Morphologically Decomposition of EEG Signals|6 Bereitschaftspotential for Rise to Stand-Up Behavior九州工業大学平成28年

Saccadic eye movements estimate prolonged time awake

Prolonged time awake increases sleep drive and causes sleepiness. Increasing sleep drive induces rapid and uncontrolled sleep initiation leading to unstable cognitive performance which is comparable to alcohol intoxication. Sleepiness causes 10 – 20 % of traffic accidents hence being a major identifiable and preventable cause of accidents. Even though the severeness of sleepiness -related accidents and hazards have been recognized and the state of New Jersey (USA) even has a law that forbids driving after being awake for more than 24 h, there is no reliable on-site test for estimating total time awake of a person. A reliable, objective, and practical metrics for measuring sleepiness outside the laboratory would be valuable. This thesis presents a novel approach and examines whether an eye movement based metric could serve as an on-site test metric for time awake. The rationale for the studying the use of eye movements to estimate overall time awake is as follows: Different cognitive functions, especially attentional ones are vulnerable to sleepiness. The attentional and oculomotor processes share neuroanatomical networks in the brain and saccadic eye movements have been used to study attentional functions. Moreover, saccadic eye movements are sensitive to sleepiness. The thesis consists of two parts: 1) Algorithm development for electro-oculographic (EOG) feature extraction to enable effective and practical analyses of measurements conducted outside the laboratory, and 2) Development of an eye movement based metric to estimate prolonged time awake. Saccadic eye movements were measured from eleven healthy adults every sixth hour with EOG in a 8-minute saccade task during 60 h of prolonged time awake. The saccade task performance, estimated as the number of saccades, decreased as a function of time awake on an individual level. The saccadic performance differed between the participants but was stable within participants (tested with 5 participants). The circadian rhythm affected the saccade task performance. Thus, the three-process model of alertness (TPMA) was fitted to, and the circadian component (C-component) was removed from, the measured data. After removing the C-component, the linear model revealed a significant trend for six out of eleven participants. The results imply that saccades measured with EOG could be used as a time awake metric outside the laboratory. The metric needs individual calibration before the time awake of a person can be estimated. More research is needed to study individual differences, optimize the measurement duration, and stimulus parameters.Pitkittynyt hereilläoloaika lisää unipainetta ja siten väsymystä. Kasvava unen tarve aiheuttaa kontrolloimattomia torkahduksia, jotka heikentävät merkittävästi ihmisen tarkkaavuutta ja siten kognitiivisia toimintoja. Univajeen aiheuttama epävakaa tila on verrattavissa humalatilaan. Liikenneonnettomuuksista 10 – 20 % on väsymyksen aiheuttamia. Väsymys on näin ollen yksi suurimmista tunnetuista, estettävissä olevista onnettomuuksien syistä. Väsymyksestä johtuvien onnettomuuksien ja katastrofien vakavuus on tunnistettu; mm. New Jerseyssä (Yhdysvallat) on säädetty laki, joka kieltää ajamisen yli 24 tunnin hereilläoloajan jälkeen. Mittalaitetta, jolla kenttäolosuhteissa pystytään mittaamaan luotettavasti, objektiivisesti ja käytännöllisesti kuljettajan hereilläolon kokonaisaikaa ei kuitenkaan ole tällä hetkellä saatavilla. Tässä väitöskirjassa on kehitetty silmänliikkeisiin perustuva mittausmenetelmä, jonka avulla voidaan mitata hereilläoloaikaa laboratorion kenttäolosuhteissa, laboratorion ulkopuolella. Univajeessa kognitiiviset toiminnot heikkenevät, erityisesti tarkkaavuus sekä visuaalinen, silmänliikkeiden avulla tapahtuva ympäristön havainnointi. Tarkkaavuutta ja okulomotorisia toimintoja säätelevät osittain samat aivojen otsalohkoalueiden hermoverkot. Tästä syystä sakkadisia silmänliikkeitä voidaan käyttää sekä tarkkaavuuden että univajeen ja väsymyksen tutkimiseen. Väitöskirja koostuu kahdesta osiosta: 1) Algoritmikehitystyöstä silmänliikkeiden tunnistamiseksi luotettavasti kenttäolosuhteissa silmänliikesignaalista, 2) Silmänliikepohjaisen menetelmän kehittäminen hereilläoloajan estimointiin. Sakkadisia silmänliikkeitä mitattiin yhdeltätoista terveeltä aikuiselta kuuden tunnin välein 60 tunnin yhtäjaksoisen univajeen aikana. Silmänliikkeet rekisteröitiin elektro-okulografia (EOG) -menetelmällä 8 minuuttia kestävän sakkaditestin aikana. Tehtävässä suoriutumista arvioitiin sen aikana suoritettujen sakkadien lukumäärällä. Sakkadien lukumäärä laski hereilläoloajan funktiona kaikilla tutkittavilla. Sakkaditehtävässä suoriutuminen vaihteli henkilöiden välillä. Testin toistettavuutta tutkittiin viidellä henkilöllä ja se todettiin toistettavaksi. Vuorokaudenaika vaikutti tehtävässä suoriutumiseen ja tästä syystä vuorokausivaihteluun liittyvä sirkadiaaninen rytmi poistettiin vireystilaa mallintavan mallin avulla (three-process model of alertness, TPMA). Sirkadiaanisen rytmin poistamisen jälkeen sakkadien lukumäärän lasku hereilläoloajan funktiona oli lineaarinen kuudella tutkimushenkilöllä yhdestätoista. Väitöskirjassa esitettyjen tulosten perusteella EOG-menetelmällä mitattujen silmänliikeiden avulla voidaan estimoida hereilläoloaikaa kenttäolosuhteissa. Tällä hetkellä mittaus vaatii henkilökohtaisen kalibrointimittauksen ennen varsinaista testimittausta. Lisää tutkimustyötä tarvitaan henkilöiden yksilöllisten erojen tutkimiseen, sekä mittausasetelman optimointiin kenttäolosuhteisiin laajemmin sopivaksi

A Deep Learning Approach for Vital Signs Compression and Energy Efficient Delivery in mhealth Systems

© 2013 IEEE. Due to the increasing number of chronic disease patients, continuous health monitoring has become the top priority for health-care providers and has posed a major stimulus for the development of scalable and energy efficient mobile health systems. Collected data in such systems are highly critical and can be affected by wireless network conditions, which in return, motivates the need for a preprocessing stage that optimizes data delivery in an adaptive manner with respect to network dynamics. We present in this paper adaptive single and multiple modality data compression schemes based on deep learning approach, which consider acquired data characteristics and network dynamics for providing energy efficient data delivery. Results indicate that: 1) the proposed adaptive single modality compression scheme outperforms conventional compression methods by 13.24% and 43.75% reductions in distortion and processing time, respectively; 2) the proposed adaptive multiple modality compression further decreases the distortion by 3.71% and 72.37% when compared with the proposed single modality scheme and conventional methods through leveraging inter-modality correlations; and 3) adaptive multiple modality compression demonstrates its efficiency in terms of energy consumption, computational complexity, and responding to different network states. Hence, our approach is suitable for mobile health applications (mHealth), where the smart preprocessing of vital signs can enhance energy consumption, reduce storage, and cut down transmission delays to the mHealth cloud.This work was supported by NPRP through the Qatar National Research Fund (a member of the Qatar Foundation) under Grant 7-684-1-127

Deep learning for healthcare applications based on physiological signals: A review

Background and objective: We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017. Methods: An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review. Results: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input. Conclusions: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosi