Abstract
A series of short events, called A-phases, can be observed in the human electroencephalogram (EEG) during Non-Rapid Eye Movement (NREM) sleep. These events can be classified in three groups (A1, A2, and A3) according to their spectral contents, and are thought to play a role in the transitions between the different sleep stages. A-phase detection and classification is usually performed manually by a trained expert, but it is a tedious and time-consuming task. In the past two decades, various researchers have designed algorithms to automatically detect and classify the A-phases with varying degrees of success, but the problem remains open. In this paper, a different approach is proposed: instead of attempting to design a general classifier for all subjects, we propose to train ad-hoc classifiers for each subject using as little data as possible, in order to drastically reduce the amount of time required from the expert. The proposed classifiers are based on deep convolutional neural networks using the log-spectrogram of the EEG signal as input data. Results are encouraging, achieving average accuracies of 80.31% when discriminating between A-phases and non A-phases, and 71.87% when classifying among A-phase sub-types, with only 25% of the total A-phases used for training. When additional expert-validated data is considered, the sub-type classification accuracy increases to 78.92%. These results show that a semi-automatic annotation system with assistance from an expert could provide a better alternative to fully automatic classifiers.
Similar content being viewed by others
References
Wolk R, Somers VK (2007) Sleep and the metabolic syndrome. Exp Physiol 92(1):67–78
David F, Dinges FP, Williams K, Gillen KA, Powell JW, Ott GE, Aptowicz C, Pack AI (1997) Cumulative sleepiness, mood disturbance, and psychomotor vigilance performance decrements during a week of sleep restricted to 4–5 hours per night. Sleep 20(4):267–277
Altevogt BM, Colten HR et al (2006) Sleep disorders and sleep deprivation: an unmet public health problem. National Academies Press
Terzano MG, Parrino L, Smerieri A, Chervin R, Chokroverty S, Guilleminault C, Hirshkowitz M, Mahowald M, Moldofsky H, Rosa A et al (2002) Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (cap) in human sleep. Sleep Med 3(2):187–199
Ferini-Strambi L, Bianchi A, Zucconi M, Oldani A, Castronovo V, Smirne S (2000) The impact of cyclic alternating pattern on heart rate variability during sleep in healthy young adults. Clin Neurophysiol 111(1):99–101
Ferri R, Parrino L, Smerieri A, Terzano MG, Elia M, Musumeci SA, Pettinato S (2000) Cyclic alternating pattern and spectral analysis of heart rate variability during normal sleep. J Sleep Res 9(1):13–18
Sforza E, Jouny C, Ibanez V (2000) Cardiac activation during arousal in humans: further evidence for hierarchy in the arousal response. Clin Neurophysiol 111(9):1611–1619
Terzano MG, Parrino L (2000) Origin and significance of the cyclic alternating pattern (cap). Sleep Med Rev 4(1):101–123
Terzano MG, Parrino L (1993) Clinical applications of cyclic alternating pattern. Physiol Behav 54(4):807–813
Ferri R, Bruni O, Miano S, Smerieri A, Spruyt K, Terzano MG (2005) Inter-rater reliability of sleep cyclic alternating pattern (cap) scoring and validation of a new computer-assisted cap scoring method. Clin Neurophysiol 116(3):696–707
Ferri R, Bruni O, Miano S, Plazzi G, Terzano MG (2005) All-night eeg power spectral analysis of the cyclic alternating pattern components in young adult subjects. Clin Neurophysiol 116(10):2429–2440
De Carli F, Nobili L, Beelke M, Watanabe T, Smerieri A, Parrino L (2004) Mario Giovanni Terzano, and Franco Ferrillo. Quantitative analysis of sleep eeg microstructure in the time–frequency domain. Brain Res Bull 63(5):399–405
Navona C, Barcaro U, Bonanni E, Di Martino F, Maestri M, Murri L (2002) An automatic method for the recognition and classification of the a-phases of the cyclic alternating pattern. Clin Neurophysiol 113(11):1826–1831
Mariani S, Grassi A, Mendez MO, Parrino L, Terzano MG, Bianchi AM (2011) Automatic detection of cap on central and fronto-central eeg leads via support vector machines. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 1491–1494. IEEE
Mariani S, Manfredini E, Rosso V, Grassi A, Mendez MO, Alba A, Matteucci M, Parrino L, Terzano MG, Cerutti S et al (2012) Efficient automatic classifiers for the detection of a phases of the cyclic alternating pattern in sleep. Med Biol Eng Comput 50(4):359–372
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press
Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR (2018) Deep learning for healthcare applications based on physiological signals: A review. Comput Methods Programs Biomed 161:1–13
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88
Shen D, Wu G, Suk H-I (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248
Yildirim Ö (2018) A novel wavelet sequence based on deep bidirectional lstm network model for ecg signal classification. Comput Biol Med 96:189–202
Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A, Tan RS (2017) A deep convolutional neural network model to classify heartbeats. Comput Biol Med 89:389–396
Yıldırım Ö, Pławiak P, Tan R-S, Acharya UR (2018) Arrhythmia detection using deep convolutional neural network with long duration ecg signals. Comput Biol Med 102:411–420
Oh SL, Hagiwara Y, Raghavendra U, Yuvaraj R, Arunkumar N, Murugappan M, Acharya UR (2018) A deep learning approach for parkinson’s disease diagnosis from eeg signals. Neural Comput Appl:1–7
Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H (2018) Deep convolutional neural network for the automated detection and diagnosis of seizure using eeg signals. Comput Biol Med 100:270–278
Yıldırım Ö, Baloglu UB, Acharya UR (2018) A deep convolutional neural network model for automated identification of abnormal eeg signals. Neural Comput Appl:1–12
Antoniades A, Spyrou L, Martin-Lopez D, Valentin A, Alarcon G, Sanei S, Took CC (2018) Deep neural architectures for mapping scalp to intracranial eeg. Int J Neural Syst 28(08):1850009
Supratak A, Dong H, Wu C, Guo Y (2017) Deepsleepnet: a model for automatic sleep stage scoring based on raw single-channel eeg. IEEE Transac Neural Syst Rehabil Eng 25(11):1998–2008
Tsinalis O, Matthews PM, Guo Y (2016) Automatic sleep stage scoring using time-frequency analysis and stacked sparse autoencoders. Ann Biomed Eng 44(5):1587–1597
Tripathy RK, Acharya UR (2018) Use of features from rr-time series and eeg signals for automated classification of sleep stages in deep neural network framework. Biocybernetics Biomed Eng 38(4):890–902
Chambon S, Galtier MN, Arnal PJ, Wainrib G, Gramfort A (2018) A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series. IEEE Transac Neural Syst Rehabil Eng 26(4):758–769
Michielli N, Acharya UR, Molinari F (2019) Cascaded lstm recurrent neural network for automated sleep stage classification using single-channel eeg signals. Comput Biol Med 106:71–81
Rosa AC, Parrino L, Terzano MG (1999) Automatic detection of cyclic alternating pattern (cap) sequences in sleep: preliminary results. Clin Neurophysiol 110(4):585–592
Rosa AC, Kemp B, Paiva T, da Silva FHL, Kamphuisen HAC (1991) A model-based detector of vertex waves and k complexes in sleep electroencephalogram. Electroencephal Clin Neurophysiol 78(1):71–79
Barcaro U, Bonanni E, Maestri M, Murri L, Parrino L, Terzano MG (2004) A general automatic method for the analysis of nrem sleep microstructure. Sleep Med 5(6):567–576
Mendez MO, Alba A, Chouvarda I, Milioli G, Grassi A, Terzano MG, Parrino L (2014) On separability of a-phases during the cyclic alternating pattern. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 2253–2256. IEEE
Mendez MO, Chouvarda I, Alba A, Bianchi AM, Grassi A, Arce-Santana E, Milioli G, Terzano MG, Parrino L (2016) Analysis of a-phase transitions during the cyclic alternating pattern under normal sleep. Med Biol Eng Comput 54(1):133–148
Karimzadeh F, Seraj E, Boostani R, Torabi-Nami M (2015) Presenting efficient features for automatic cap detection in sleep eeg signals. In: 2015 38th International Conference on Telecommunications and Signal Processing (TSP), pp 448–452. IEEE
Mendonça F, Fred A (2018) Sheikh Shanawaz Mostafa, Fernando Morgado-Dias, and Antonio G Ravelo-García. Automatic detection of cyclic alternating pattern. Neural Comput & Applic:1–11
Machado F, Teixeira C, Santos C, Bento C, Sales F, Dourado A (2016) A-phases subtype detection using different classification methods. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 1026–1029. IEEE
Machado F, Sales F, Santos C, Dourado A, Teixeira CA (2018) A knowledge discovery methodology from eeg data for cyclic alternating pattern detection. Biomed Eng Online 17(1):185
Mostafa SS, Mendonça F, Ravelo-García A, Morgado-Dias F (2018) Combination of deep and shallow networks for cyclic alternating patterns detection. In: 2018 13th APCA International Conference on Control and Soft Computing (CONTROLO), pp 98–103. IEEE
Hartmann S, Baumert M (2019) Automatic a-phase detection of cyclic alternating patterns in sleep using dynamic temporal information. IEEE Transac Neural Syst Rehabil Eng 27(9):1695–1703
Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE (2000) Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220
Conrad Iber The aasm manual for the scoring of sleep and associated events: rules. Terminology and Technical Specification, 2007
Haghighi-Mood A, Torry JN (1997) Time frequency analysis of systolic murmurs. time-frequency analysis of biomedical signals. In: IEE Colloquium on Year, pp 2/1–2/3. IEE
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: NIPS’12 Proceedings of the 25th International Conference on Neural Information Processing Systems, pp 1097–1105. NIPS
I. Arel, D. Rose, and T. Karnowski. Deep machine learning–a new frontier in artificial intelligence research [research frontier]. IEEE Comput Int Mag, 5:13–18, 2010
Ciresan D, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. In: 2012 Computer Vision and Pattern Recognition (CVPR), pp 3642–3649. IEEE
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Arce-Santana, E.R., Alba, A., Mendez, M.O. et al. A-phase classification using convolutional neural networks. Med Biol Eng Comput 58, 1003–1014 (2020). https://doi.org/10.1007/s11517-020-02144-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11517-020-02144-6