scholarly journals Automated Detection and Classification of Sleep Apnea Types Using Electrocardiogram (ECG) and Electroencephalogram (EEG) Features

10.5772/22782 ◽  
2012 ◽  
Author(s):  
Onur Kocak ◽  
Tuncay Bayrak ◽  
Aykut Erdamar ◽  
Levent Ozparlak ◽  
Ziya Telatar ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Automated Detection ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg ◽  
Eeg Features

scholarly journals EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Bingtao Zhang ◽  
Tao Lei ◽  
Hong Liu ◽  
Hanshu Cai
Keyword(s):  
Visual Inspection ◽  
Sleep Stages ◽  
Subjective Time ◽  
Sleep Staging ◽  
Large Bulk ◽  
State Classification ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
Eeg Features

Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of tradition is subjective, time-consuming, and error-prone due to the large bulk of data which have to be processed. Therefore, automatic sleep staging is essential in order to solve these problems. In this article, an electroencephalogram- (EEG-) based scheme that is able to automatically classify sleep stages is proposed. Firstly, EEG data are preprocessed to remove artifacts, extract features, and normalization. Secondly, the normalized features and other context information are stored using an ontology-based model (OBM). Thirdly, an improved method of self-adaptive correlation analysis is designed to select the most effective EEG features. Based on these EEG features and weighting features analysis, the improved random forest (RF) is considered as the classifier to achieve the classification of sleep stages. To investigate the classification ability of the proposed method, several sets of experiments are designed and conducted to classify the sleep stages into two, three, four, and five states. The accuracy of five-state classification is 89.37%, which is improved compared to the accuracy using unimproved RF (84.37%) or previously reported classifiers. In addition, a set of controlled experiments is executed to verify the effect of the number of sleep segments (epochs) on the classification, and the results demonstrate that the proposed scheme is less affected by the sleep segments.

scholarly journals Classification of Sleep Apnea Based on Sub-Band Decomposition of EEG Signals

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1571
Author(s):  
Rajeswari Jayaraj ◽  
Jagannath Mohan
Keyword(s):  
Support Vector Machine ◽  
Sleep Apnea ◽  
Support Vector Machine Classifier ◽  
Wavelet Packet ◽  
Brain Perfusion ◽  
Support Vector ◽  
Eeg Signals ◽  
Data Format ◽  
Electroencephalogram Eeg

To classify between normal and sleep apnea subjects based on sub-band decomposition of electroencephalogram (EEG) signals. This study comprised 159 subjects obtained from the ISRUC (Institute of System and Robotics—University of Coimbra), Sleep-EDF (European Data Format), and CAP (Cyclic Alternating Pattern) Sleep database, which consists of normal and sleep apnea subjects. The wavelet packet decomposition method was incorporated to categorize the EEG signals into five frequency bands, namely, alpha, beta, delta, gamma, and theta. Entropy and energy (non-linear) for all bands was calculated and as a result, 10 features were obtained for each EEG signal. The ratio of EEG bands included four parameters, including heart rate, brain perfusion, neural activity, and synchronization. In this study, a support vector machine with kernels and random forest classifiers was used for classification. The performance measures demonstrated that the improved results were obtained from the support vector machine classifier with a kernel polynomial order 2. The accuracy (90%), sensitivity (100%), and specificity (83%) with 14 features were estimated using the data obtained from ISRUC database. The proposed study is feasible and seems to be accurate in classifying the subjects with sleep apnea based on the extracted features from EEG signals using a support vector machine classifier.

Classification of Epileptic Seizure in EEG Signal Using Support Vector Machine and EMD

2020 ◽  
pp. 80-95
Author(s):  
Virender Kumar Mehla ◽  
Ashish Kumar ◽  
Amit Singhal ◽  
Pushpendra Singh ◽  
Manjeet Kumar ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Epileptic Activity ◽  
Support Vector ◽  
Eeg Signal ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Last Stage ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg

With the rapid innovation in the field of healthcare, various biomedical signals, namely, electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), play a crucial role for accurate measurement of various diseases such as cardiovascular diseases, brain disorders, etc. In the present work, an efficient method based on empirical mode decomposition (EMD) has been proposed to detect the epileptic activity. The present study is composed of three parts. In the first part, EMD is used to decompose the EEG signal into a set of amplitude modulated and frequency modulated components, referred to as intrinsic mode functions (IMFs). In the second part, features such as standard deviation, kurtosis, and Hjorth parameters have been extracted from various IMFs. In the last stage, the features are employed as inputs to support vector machine classifier for classification between non-seizure and seizure EEG signals. The simulation results show that the proposed scheme has attained better classification accuracy when compared to existing state-of-the-art methods.

Automated detection and classification of shoulder arthroplasty models using deep learning

2020 ◽  
Vol 49 (10) ◽  
pp. 1623-1632
Author(s):  
Paul H. Yi ◽  
Tae Kyung Kim ◽  
Jinchi Wei ◽  
Xinning Li ◽  
Gregory D. Hager ◽  
...  
Keyword(s):  
Deep Learning ◽  
Shoulder Arthroplasty ◽  
Automated Detection

scholarly journals Detecting Apnea/Hypopnea Events Time Location from Sound Recordings for Patients with Severe or Moderate Sleep Apnea Syndrome

2021 ◽  
Vol 11 (15) ◽  
pp. 6888
Author(s):  
Georgia Korompili ◽  
Lampros Kokkalas ◽  
Stelios A. Mitilineos ◽  
Nicolas-Alexander Tatlas ◽  
Stelios M. Potirakis
Keyword(s):  
Sleep Apnea ◽  
Sleep Apnea Syndrome ◽  
Apnea Hypopnea Index ◽  
Event Time ◽  
Severity Estimation ◽  
Apnea Duration ◽  
Respiratory Events ◽  
Moderate Sensitivity ◽  
Apnea Syndrome

The most common index for diagnosing Sleep Apnea Syndrome (SAS) is the Apnea-Hypopnea Index (AHI), defined as the average count of apnea/hypopnea events per sleeping hour. Despite its broad use in automated systems for SAS severity estimation, researchers now focus on individual event time detection rather than the insufficient classification of the patient in SAS severity groups. Towards this direction, in this work, we aim at the detection of the exact time location of apnea/hypopnea events. We particularly examine the hypothesis of employing a standard Voice Activity Detection (VAD) algorithm to extract breathing segments during sleep and identify the respiratory events from severely altered breathing amplitude within the event. The algorithm, which is tested only in severe and moderate patients, is applied to recordings from a tracheal and an ambient microphone. It proves good sensitivity for apneas, reaching 81% and 70.4% for the two microphones, respectively, and moderate sensitivity to hypopneas—approx. 50% were identified. The algorithm also presents an adequate estimator of the Mean Apnea Duration index—defined as the average duration of the detected events—for patients with severe or moderate apnea, with mean error 1.7 s and 3.2 s for the two microphones, respectively.

Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review

2021 ◽  
Author(s):  
Hamdi Altaheri ◽  
Ghulam Muhammad ◽  
Mansour Alsulaiman ◽  
Syed Umar Amin ◽  
Ghadir Ali Altuwaijri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Learning Techniques ◽  
Electroencephalogram Eeg

Automated detection and classification of spilled loads on freeways based on improved YOLO network

2021 ◽  
Vol 32 (2) ◽  
Author(s):  
Siqi Zhou ◽  
Yufeng Bi ◽  
Xu Wei ◽  
Jiachen Liu ◽  
Zixin Ye ◽  
...  
Keyword(s):  
Automated Detection

scholarly journals Midventricular takotsubo syndrome: different dyskinesia location leading to a different electrocardiogram?

2021 ◽  
Vol 10 (Supplement_1) ◽  
Author(s):  
M Padilla Lopez ◽  
A Duran Cambra ◽  
M Vidal Burdeus ◽  
L Rodriguez Sotelo ◽  
J Sanchez Vega ◽  
...  
Keyword(s):  
Acute Phase ◽  
Clinical Symptoms ◽  
Takotsubo Syndrome ◽  
St Segment Elevation ◽  
Funding Sources ◽  
Wave Inversion ◽  
St Segment ◽  
Electrocardiogram Ecg ◽  
Study Inclusion

Abstract Funding Acknowledgements Type of funding sources: None. Background Takotsubo syndrome (TKS) is characterized by the appearance of apical reversible dyskinesia in its typical form. Electrocardiogram (ECG) in the acute phase (<12 from symptom onset) generally shows anterior ST segment elevation. Nonetheless, other atypical forms of TKS have been described depending on the location of the dyskinetic segments, such as, mid-ventricular, basal and focal forms. Considering the different segments involved in these atypical forms, it seems reasonable to consider that ST changes in acute phase ECG could be different. Purpose To compare ECG in the acute phase of typical TKS versus mid-ventricular TKS, as it was the more frequent form of atypical TKS in our registry. Methods Patients included in the prospective TKS registry of our center according to the Mayo Clinic diagnostic criteria, with the first ECG performed less than 12 hours from the symptoms onset were reviewed. All cardiac left ventriculographies were reviewed to ensure a correct classification of the different types of TKS. Results A total of 297 patients were included in our local registry. 80 patients met our study inclusion criteria. 56 ECGs of typical apical TKS were compared to 24 ECGs of atypical midventricular TKS. There were no differences between the baseline characteristics in both groups, except for mid-ventricular TKS, that was more frequently triggered by physical stressor. Regarding the ECG analysis, the main difference found in our serie was related to ST-segment deviation (Table 1). While ST-segment elevation was more common in typical TKS than in atypical TKS (73% vs 50%), ST-segment depression (generally in inferior leads) was observed in 54% of patients with atypical TKS and in no patient with typical TKS (figure 1). Conclusion The different location of dyskinesia between typical TKS and mid-ventricular TKS is associated to significant differences in the ECG obtained in the first hours after the onset of the clinical symptoms. The presence of ST-segment depression is highly suggestive of mid-ventricular TKS. ECG characteristicsTypical (n = 56)Midventricular (n = 24)pSTe > 1mm, no (%)41 (73)12 (50)0,044STd >0,5 mm, no (%)013 (54)< 0,001T wave inversion, no (%)12 (21)4 (17)0,626Q wave, no (%)22 ( 39)12 (50)0,374cQT, mean (SD)445 (54)438 (37)0,578QRS low voltages*, n (%)9 ( 16)1 (4)0,328STe ST-segment elevation, STd: ST-segment depression, cQT: corrected QT interval *Voltages <5mm in all limb leads or <10mm in all precordial leads Abstract Figure. 12-lead ECG and left ventriculography

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