Pub Date : 2020-11-26DOI: 10.1109/ICBME51989.2020.9319415
Shahla Bakian Dogaheh, M. Hassan Moradi
In this paper, we propose a system to perform automatic sleep stage classification based on physiological signals acquired by Dreem Headband. These signals contain 4 EEG (FpZ-O1, FpZ-O2, FpZ-F7, F8-F7), 2 Pulse oximeter (Red & Infra-red), and 3 accelerometer channels (X, Y, Z). The dataset used in this study belongs to a challenge competition, namely as Challenge Data and is publicly available on their website. In this work, sleep stages have been scored according to the AASM standard. Features were extracted from the physiological signals after applying a preprocessing step. Each of the EEG and PPG’s features is falling into one of the three categories time, frequency, or entropy. Moreover, ancillary features were also extracted from the accelerometer signal. Extracted features were classified by using support vector machine (SVM), K-nearest neighbor and Random forest classifiers. Due to the class imbalance problem, stratified 5-fold cross-validation was performed in order to tune systems parameters. Results show that among the three models as mentioned above, Random Forest has the best performance for the 5-class classification with accuracy: 79.98± 0.70 and kappa 0.7234±0.0095. The proposed model shows promising results, thus the model can be implemented in Dreem headband to differentiate sleep stages efficiently and be used in clinical applications.
{"title":"Automatic sleep stage classification based on Dreem headband’s signals","authors":"Shahla Bakian Dogaheh, M. Hassan Moradi","doi":"10.1109/ICBME51989.2020.9319415","DOIUrl":"https://doi.org/10.1109/ICBME51989.2020.9319415","url":null,"abstract":"In this paper, we propose a system to perform automatic sleep stage classification based on physiological signals acquired by Dreem Headband. These signals contain 4 EEG (FpZ-O1, FpZ-O2, FpZ-F7, F8-F7), 2 Pulse oximeter (Red & Infra-red), and 3 accelerometer channels (X, Y, Z). The dataset used in this study belongs to a challenge competition, namely as Challenge Data and is publicly available on their website. In this work, sleep stages have been scored according to the AASM standard. Features were extracted from the physiological signals after applying a preprocessing step. Each of the EEG and PPG’s features is falling into one of the three categories time, frequency, or entropy. Moreover, ancillary features were also extracted from the accelerometer signal. Extracted features were classified by using support vector machine (SVM), K-nearest neighbor and Random forest classifiers. Due to the class imbalance problem, stratified 5-fold cross-validation was performed in order to tune systems parameters. Results show that among the three models as mentioned above, Random Forest has the best performance for the 5-class classification with accuracy: 79.98± 0.70 and kappa 0.7234±0.0095. The proposed model shows promising results, thus the model can be implemented in Dreem headband to differentiate sleep stages efficiently and be used in clinical applications.","PeriodicalId":120969,"journal":{"name":"2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131064828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-26DOI: 10.1109/ICBME51989.2020.9319416
S. Afshar, R. Boostani
Controlling the depth of anesthesia (DOA) through long surgeries is a crucial issue, and inaccurate dosage of pain killer and other anesthetic agents may lead to awareness or comma. Nonetheless, the accurate monitoring of DOA by analyzing electroencephalography (EEG) is still a challenge. To mimic the bispectral index (BIS) this study presents a deep learning method, which receives two EEG channels (located on the forehead) and continuously predicts the BIS score. The proposed method consists of convolutional neural network (residual network) followed by a recurrent neural network (bidirectional long short-term memory). In addition, we compare the performance of the proposed network with conventional methods in terms of regression and classification errors. All of the models are applied to a big dataset contains 176 subjects. The proposed network outperforms the conventional methods with respect to the generalization and both errors.
{"title":"A Two-Stage Deep Learning Scheme to Estimate Depth of Anesthesia from EEG Signals","authors":"S. Afshar, R. Boostani","doi":"10.1109/ICBME51989.2020.9319416","DOIUrl":"https://doi.org/10.1109/ICBME51989.2020.9319416","url":null,"abstract":"Controlling the depth of anesthesia (DOA) through long surgeries is a crucial issue, and inaccurate dosage of pain killer and other anesthetic agents may lead to awareness or comma. Nonetheless, the accurate monitoring of DOA by analyzing electroencephalography (EEG) is still a challenge. To mimic the bispectral index (BIS) this study presents a deep learning method, which receives two EEG channels (located on the forehead) and continuously predicts the BIS score. The proposed method consists of convolutional neural network (residual network) followed by a recurrent neural network (bidirectional long short-term memory). In addition, we compare the performance of the proposed network with conventional methods in terms of regression and classification errors. All of the models are applied to a big dataset contains 176 subjects. The proposed network outperforms the conventional methods with respect to the generalization and both errors.","PeriodicalId":120969,"journal":{"name":"2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132821768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-26DOI: 10.1109/ICBME51989.2020.9319324
Mohsen Annabestani, F. Esmaeili, Nooshin Orouji, Pouria Esmaeili-Dokht, M. Fardmanesh
In this paper, a simple, low-cost, and fast prototyping method is presented for digital microfluidics (DMFs) fabrication. In the proposed method, commercial Polymethyl methacrylate (PMMA) sheets are used as the substrate, and a desktop diode laser engraver has replaced photolithography for electrode patterning. The proposed maskless method is capable of forming the DMF electrodes with ~100µm spacing. Engraved patterns are filled with a conductive paste like Silver paste to form electrodes. A thin layer of Polydimethylsiloxane (PDMS) and Bio-Oil® are exploited as dielectric and hydrophobic layer, respectively. Fabricated devices were successfully tested for droplet manipulation and mixing which shows that this method can be a rapid and low-cost alternative for conventional fabrication techniques.
{"title":"Rapid prototyping of low-cost digital microfluidic devices using laser ablation","authors":"Mohsen Annabestani, F. Esmaeili, Nooshin Orouji, Pouria Esmaeili-Dokht, M. Fardmanesh","doi":"10.1109/ICBME51989.2020.9319324","DOIUrl":"https://doi.org/10.1109/ICBME51989.2020.9319324","url":null,"abstract":"In this paper, a simple, low-cost, and fast prototyping method is presented for digital microfluidics (DMFs) fabrication. In the proposed method, commercial Polymethyl methacrylate (PMMA) sheets are used as the substrate, and a desktop diode laser engraver has replaced photolithography for electrode patterning. The proposed maskless method is capable of forming the DMF electrodes with ~100µm spacing. Engraved patterns are filled with a conductive paste like Silver paste to form electrodes. A thin layer of Polydimethylsiloxane (PDMS) and Bio-Oil® are exploited as dielectric and hydrophobic layer, respectively. Fabricated devices were successfully tested for droplet manipulation and mixing which shows that this method can be a rapid and low-cost alternative for conventional fabrication techniques.","PeriodicalId":120969,"journal":{"name":"2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126587202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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