Evaluating the use of line length for automatic sleep spindle detection

Imtiaz, Syed Anas; Rodriguez‐Villegas, Esther

doi:10.1109/embc.2014.6944753

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…The difference between SEF95 and SEF50 (known as SEFd) in 8-16 Hz frequency band, shown to be highly useful for detection of REM sleep [19] was also included in this set. Further, line length of the signal in 11-16 Hz range was also added since it is useful for sleep spindle detection [20], thereby helpful for scoring N2 and N3 stages.…”

Section: B Featuresmentioning

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: B Featuresmentioning

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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“…The hierarchical fusion algorithm is a favorable and feasible method for liberating the “gold standard” detection of experts, and reducing the shortcomings of the cumbersome, expensive, and strongly subjective spindle detection methods of the past ( Parekh et al, 2017 ). This method could be popularized for clinical disease diagnosis instead of artificial spindle detection as it improves the speed of disease diagnosis and enables patients to receive rapid treatment ( Imtiaz and Rodriguez-Villegas, 2014 ). At the same time, according to this test, the study of spindles on human intelligence and memory can save substantial experimental time ( Wei et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Hierarchical fusion detection algorithm for sleep spindle detection

Chen

Meng²,

Belkacem³

et al. 2023

Front. Neurosci.

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BackgroundSleep spindles are a vital sign implying that human beings have entered the second stage of sleep. In addition, they can effectively reflect a person’s learning and memory ability, and clinical research has shown that their quantity and density are crucial markers of brain function. The “gold standard” of spindle detection is based on expert experience; however, the detection cost is high, and the detection time is long. Additionally, the accuracy of detection is influenced by subjectivity.MethodsTo improve detection accuracy and speed, reduce the cost, and improve efficiency, this paper proposes a layered spindle detection algorithm. The first layer used the Morlet wavelet and RMS method to detect spindles, and the second layer employed an improved k-means algorithm to improve spindle detection efficiency. The fusion algorithm was compared with other spindle detection algorithms to prove its effectiveness.ResultsThe hierarchical fusion spindle detection algorithm showed good performance stability, and the fluctuation range of detection accuracy was minimal. The average value of precision was 91.6%, at least five percentage points higher than other methods. The average value of recall could reach 89.1%, and the average value of specificity was close to 95%. The mean values of accuracy and F1-score in the subject sample data were 90.4 and 90.3%, respectively. Compared with other methods, the method proposed in this paper achieved significant improvement in terms of precision, recall, specificity, accuracy, and F1-score.ConclusionA spindle detection method with high steady-state accuracy and fast detection speed is proposed, which combines the Morlet wavelet with window RMS and an improved k-means algorithm. This method provides a powerful tool for the automatic detection of spindles and improves the efficiency of spindle detection. Through simulation experiments, the sampled data were analyzed and verified to prove the feasibility and effectiveness of this method.

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“…Discrete hardware based approaches for spindle detection have been introduced by [7], [8] and [9]. They all use low complexity algorithms implemented using off-the-shelf components.…”

Section: Introductionmentioning

confidence: 99%

“…They all use low complexity algorithms implemented using off-the-shelf components. Out of those, the only system that had power consumption as a target was the one in [9]. This system was based on the implementation of a low complexity line-length feature based algorithm on a TI MSP40 microcontroller.…”

Section: Introductionmentioning

confidence: 99%

An Ultralow-Power Sleep Spindle Detection System on Chip

Iranmanesh

Rodriguez‐Villegas

2017

IEEE Trans. Biomed. Circuits Syst.

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Abstract-This paper describes a full system-on-chip to automatically detect sleep spindle events from scalp EEG signals. These events, which are known to play an important role on memory consolidation during sleep, are also characteristic of a number of neurological diseases. The operation of the system is based on a previously reported algorithm which used the Teager Energy Operator (TEO), together with the Spectral Edge Frequency (SEF50) achieving over 70% sensitivity and 98% specificity. The algorithm is now converted into a hardware analog based customized implementation in order to achieve extremely low levels of power. Experimental results prove that the system, which is fabricated in a 0.18 µm technology, is able to operate from a 1.25 V power supply consuming only 515 nW, with an accuracy which is comparable to its software counterpart.

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Evaluating the use of line length for automatic sleep spindle detection

Cited by 5 publications

References 15 publications

Automatic sleep staging using state machine-controlled decision trees

Automatic sleep staging using state machine-controlled decision trees

Hierarchical fusion detection algorithm for sleep spindle detection

An Ultralow-Power Sleep Spindle Detection System on Chip

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