Development and comparison of four sleep spindle detection methods

Huupponen, Eero; Gómez-Herrero, Germán; Saastamoinen, A.; Värri, Alpo; Hasan, Joel; Himanen, Sari-Leena

doi:10.1016/j.artmed.2007.04.003

Cited by 90 publications

(91 citation statements)

References 25 publications

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“…If such a criterion was used to evaluate our algorithm, it would have shown a 4.3% false positive rate for the first study and 1.7% for the second one, hence tending to be lower than the one from Huupponen et al (2000a). The same group proposed another algorithm (Huupponen et al, 2007) with a true positive rate ranging between 51.2% and 86.5% and a false positive rate ranging between 26.4% and 46.0%. Their definition of the false positive rate differed from ours and from the one used in their previous work, as the false positive rate is set to the ratio of FP to the sum of TP and FP.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the 'classical' 12-14 Hz spindle definition is believed to be too narrow (Jankel and Niedermeyer, 1985). The difficulty in finding the optimum frequency bounds has produced a large number of proposed values, among them: 11.5-15 Hz (Fish et al, 1988), 11.5-16 Hz (Zeitlhofer et al, 1997), 11-15 Hz (Ktonas et al, 2009), 11-16 Hz (Clemens et al, 2005, 10.5-16 Hz (Ventouras et al, 2005;Huupponen et al, 2007), and 10-16 Hz (Zygierewicz et al, 1999;Huupponen et al, 2000a;Estévez et al, 2002). Beside the often cited 12-14 Hz frequency range proposed by the National Institute of Neurological Diseases and Blindness of the U.S. Department of Health, Education, and Welfare (Rechtschaffen and Kales, 1968), various organizations have suggested other values to score spindles: 11-16 Hz by the American Academy of Sleep Medicine (Iber et al, 2007), 11-15 Hz by the International Federation of Clinical Neurophysiology (Noachtar et al, 1999), and 12-16 Hz by the Japanese Society of Sleep Research (Hori et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

“…Recent ones are based on methods that include fuzzy logic (Huupponen et al, 2000a(Huupponen et al, , 2003, neural network (Shimada et al, 2000;Huupponen et al, 2000b;Acir and Güzeliş , 2004;Ventouras et al, 2005;Güneş et al, 2011), bandpass filter (Clemens et al, 2005;Huupponen et al, 2007), fast time frequency transform (Knoblauch et al, 2003a(Knoblauch et al, , 2003b, Fourier transform (Huupponen et al, 2007;Duman et al, 2009), wavelet transform (Duman et al, 2009), Gabor transform (Schönwald et al, 2003) and matching pursuit (Durka et al, 2005;Schönwald et al, 2006;Ktonas et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Sleep spindle detection through amplitude–frequency normal modelling

Nonclercq

Urbain

Verheulpen

et al. 2013

Journal of Neuroscience Methods

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h i g h l i g h t sAn automated model-based spindle detection algorithm is proposed. It models the amplitude-frequency spindle distribution with a bivariate normal distribution. It automatically adapts to each individual subject and derivation. It was tested in seven healthy children and six adult patients suffering from different pathologies, and performs similarly or better than sleep experts. Normal modelling enhances spindle detection quality compared to fixed amplitude and frequency thresholds. t r a c tManual scoring of sleep spindles can be very time-consuming, and achieving accurate manual scoring on a long-term recording requires high and sustained levels of vigilance, which makes it a highly demanding task with the associated risk of decreased diagnosis accuracy. Although automatic spindle detection would be attractive, most available algorithms are sensitive to variations in spindle amplitude and frequency that occur between both subjects and derivations, reducing their effectiveness.We propose here an algorithm that models the amplitude-frequency spindle distribution with a bivariate normal distribution (one normal model per derivation). Subsequently, spindles are detected when their amplitude-frequency characteristics are included within a given tolerance interval of the corresponding model. As a consequence, spindle detection is not directly based on amplitude and frequency thresholds, but instead on a spindle distribution model that is automatically adapted to each individual subject and derivation.The algorithm was first assessed against the scoring of one sleep scoring expert on EEG samples from seven healthy children. Afterward, a second study compared performance of two additional experts versus the algorithm on a dataset of six EEG samples from adult patients suffering from different pathologies, to submit the method to more challenging and clinically realistic conditions. Smaller and shorter spindles were more difficult to evaluate, as false positives and false negatives showed lower amplitude and smaller length than true positives. In both studies, normal modelling enhanced performance compared to fixed amplitude and frequency thresholds. Normal modelling is therefore attractive, as it enhances spindle detection quality.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sleep spindle detection through amplitude–frequency normal modelling

Nonclercq

Urbain

Verheulpen

et al. 2013

Journal of Neuroscience Methods

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“…1a. The number of sleep spindles observed during an overnight sleep is in the range of 200-1000 [6]. Hence, their manual identification is a laborious and error-prone task.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the use of line length for automatic sleep spindle detection

Imtiaz

Rodriguez‐Villegas

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Sleep spindles are transient waveforms observed on the electroencephalogram (EEG) during the N2 stage of sleep. In this paper we evaluate the use of line length, an efficient and low-complexity time domain feature, for automatic detection of sleep spindles. We use this feature with a simple algorithm to detect spindles achieving sensitivity of 83.6% and specificity of 87.9%. We also present a comparison of these results with other spindle detection methods evaluated on the same dataset. Further, we implemented the algorithm on a MSP430 microcontroller achieving a power consumption of 56.7 µW. The overall detection performance, combined with the low power consumption show that line length could be a useful feature for detecting sleep spindles in wearable and resource-constrained systems.

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“…A typical night's sleep EEG recording contains 200-1000 spindles [2]. They are used by sleep specialists as one of the characteristic features when determining the appropriate stage of sleep.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of sleep spindles using Teager energy and spectral edge frequency

Imtiaz

Saremi-Yarahmadi

Rodriguez‐Villegas

2013

2013 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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Abstract-Sleep spindles are the hallmark of N2 stage of sleep. They are transient waveforms observed on sleep electroencephalogram and their identification is required for sleep staging. Due to the large number of sleep spindles appearing on an overnight sleep EEG, automating the detection of sleep spindles would be desirable, not only to save specialist time but also for fully automated sleep staging systems. A simple algorithm for automatic sleep spindle detection is presented in this paper using only one channel of EEG input. This algorithm uses Teager energy and spectral edge frequency to mark sleep spindles and results in a sensitivity of 80% and specificity of about 98%. It is also shown that more than 91% of spindles detected by the algorithm were in N2 and N3 stages combined.

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Development and comparison of four sleep spindle detection methods

Cited by 90 publications

References 25 publications

Sleep spindle detection through amplitude–frequency normal modelling

Sleep spindle detection through amplitude–frequency normal modelling

Evaluating the use of line length for automatic sleep spindle detection

Automatic detection of sleep spindles using Teager energy and spectral edge frequency

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