Automated sleep stage scoring by decision tree learning

Hanaoka, Masaaki; Kobayashi, M.; Yamazaki, Haruaki

doi:10.1109/iembs.2001.1020556

Cited by 15 publications

(17 citation statements)

References 4 publications

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“…Some of the algorithms proposed recently include the use of support vector machines [4]- [6], hidden Markov models [7] and frequency coupling with linear discriminant analysis classification [8]. Other methods have also used artificial neural networks [2], [9], [10] and decision trees [11], [12] for classification with a variety of time, frequency, entropy [13], [14] and wavelet [10] sleep data either recorded by the researchers or using signals available from public sleep databases. It is generally difficult to compare the performance of algorithms that have been evaluated using different databases.…”

Section: Introductionmentioning

confidence: 99%

Automatic sleep staging using state machine-controlled decision trees

Imtiaz¹,

Rodriguez‐Villegas²

2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-Automatic sleep staging from a reduced number of channels is desirable to save time, reduce costs and make sleep monitoring more accessible by providing home-based polysomnography. This paper introduces a novel algorithm for automatic scoring of sleep stages using a combination of small decision trees driven by a state machine. The algorithm uses two channels of EEG for feature extraction and has a state machine that selects a suitable decision tree for classification based on the prevailing sleep stage. Its performance has been evaluated using the complete dataset of 61 recordings from PhysioNet Sleep EDF Expanded database achieving an overall accuracy of 82% and 79% on training and test sets respectively. The algorithm has been developed with a very small number of decision tree nodes that are active at any given time making it suitable for use in resource-constrained wearable systems.

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Section: Introductionmentioning

confidence: 99%

Automatic sleep staging using state machine-controlled decision trees

Imtiaz¹,

Rodriguez‐Villegas²

2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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“…On our database, overall 76% AR is achieved for symbolic fusion, which is better in comparison to other decision support algorithms, as reported in [10,18,19]. It proved to be an effective method in comparison to decision tree method, which has AR up to 70% [10].…”

Section: Resultsmentioning

confidence: 55%

“…In this paper, it is worth to mention that the present study consists of variety of subjects (i.e., male/female, apnea/non-apnea, and young/old), contrary to most of the studies which only consider healthy subjects [10,12]. …”

Section: Resultsmentioning

confidence: 99%

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Symbolic Fusion: A Novel Decision Support Algorithm for Sleep Staging Application

Chen¹,

Xue²,

Ugon³

et al. 2015

EAI Endorsed Trans Perv Health Tech

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With the rapid extension of clinical data and knowledge, decision making becomes a complex task for manual sleep staging. In this process, there is a need for integrating and analyzing information from heterogeneous data sources with high accuracy. This paper proposes a novel decision support algorithm-Symbolic Fusion for sleep staging application. The proposed algorithm provides high accuracy by combining data from heterogeneous sources, like EEG, EOG and EMG. This algorithm is developed for implementation in portable embedded systems for automatic sleep staging at low complexity and cost. The proposed algorithm proved to be an efficient design support method and achieved up to 76% overall agreement rate on our database of 12 patients.

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“…These motivations has led to a steady growth in different algorithms for automatic scoring of sleep stages being proposed by various research groups. These work by extracting a wide range of characteristic features from the signals and classifying them in using different methods including decision trees [3], [4] networks [5], [6], support vector machines [7] and many others. The performance of these algorithms are usually evaluated using data that has been acquired as part of their research work or by using publicly available sleep databases.…”

Section: Introductionmentioning

confidence: 99%

An open-source toolbox for standardized use of PhysioNet Sleep EDF Expanded Database

Imtiaz¹,

Rodriguez‐Villegas²

2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-PhysioNet Sleep EDF database has been the most popular source of data used for developing and testing many automatic sleep staging algorithms. However, the recordings from this database has been used in an inconsistent fashion. For example, arbitrary selection of start and end times from long term recordings, data-hypnogram mismatches, different performance metrics and hypnogram conversion from R&K to AASM. All these differences result in different data sections and performance metrics being used by researchers thereby making any direct comparison between algorithms very difficult. Recently, a superset of this database has been made available on PhysioNet, known as the Sleep EDF Expanded Database which includes 61 recordings. This provides an opportunity to standardize the way in which signals from this database should be used. With this goal in mind, we present in this paper a toolbox for automatically downloading and extracting recordings from the Sleep EDF Expanded database and converting them to a suitable format for use in MATLAB. This toolbox contains functions for selecting appropriate data for sleep analysis (based on our previous recommendations for sleep staging), hypnogram conversion and computation of performance metrics. Its use makes it simpler to start using the new sleep database and also provides a foundation for muchneeded standarization in this research field.

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Automated sleep stage scoring by decision tree learning

Cited by 15 publications

References 4 publications

Automatic sleep staging using state machine-controlled decision trees

Automatic sleep staging using state machine-controlled decision trees

Symbolic Fusion: A Novel Decision Support Algorithm for Sleep Staging Application

An open-source toolbox for standardized use of PhysioNet Sleep EDF Expanded Database

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