Heart rate spectrum analysis for sleep quality detection

Scherz, Wilhelm Daniel; Fritz, Daniel; Velicu, Oana Ramona; Seepold, Ralf; Madrid, Natividad Martínez

doi:10.1186/s13639-017-0072-z

Cited by 18 publications

(10 citation statements)

References 10 publications

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“…In the [30] study, REM sleep was determined with an adaptive threshold applied to the acquired feature. Only the heart rate signal feature: LF/HF ratio, the relative peak frequency power in the HF band, the variability within the HF band, derived from electrocardiogram (ECG), to perform classification [31]. The authors tried to detect sleep and wakefulness using a combination of ECG, actigraphy, and respiratory signals [32].…”

Section: Feature Analysis and Extractionmentioning

confidence: 99%

Sleep Stage Classification based on Two-Cycle Sleep Model Recognition using Continuous Heart Rate Variability

H¹,

Guo²,

Wang³

2020

Preprint

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：Sleep stage on the whole night is not steady. Sleepers generally pass through three to five cycles. In each cycle, there are occur four typical sleep stages, such as wake stage (WS), light stage (LS), deep sleep (DS), rapid eye movement sleep stage (REM). According to the natural routine, in this paper, we investigate the stage transition and analyze the feature of stage transition using the local cluster Algorithm (LCA). Two-cycle sleep model (TCSM) is proposed to automatically classify sleep stages using over-night continuous heart rate variability (HRV) data. The generated model is based on the characteristics of the nested cycle's sleep stage distribution and the transition probabilities of sleep stages. Experiments were conducted using a public data set including 400 healthy subjects (female 239, male 161) and the model’s classification accuracy was evaluated for four sleep stages: WS, LS, DS, REM. The experimental results showed that based on the TCSM model, the segmentation classification of pure sleep is 5.2% higher than that of the traditional method, and the accuracy of segmentation classification is 11.2% higher than the traditional sleep staging accuracy. The experimental performance is promising in terms of the accuracy, sensitivity, and specificity rates compared with the ones of the state-of-the-art methods. The study contributes to improve the quality of sleep monitoring in daily life using easy-to-wear HRV sensors.

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Section: Feature Analysis and Extractionmentioning

confidence: 99%

Sleep Stage Classification based on Two-Cycle Sleep Model Recognition using Continuous Heart Rate Variability

H¹,

Guo²,

Wang³

2020

Preprint

View full text Add to dashboard Cite

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“…(10), i.e. (13) where with the noise standard deviation σ fixed. One hundred signal realizations have been considered, and for each, the CS spectrum has been computed and the position of the contribution close to f = 0.1 Hz has been measured.…”

Section: (A) (B)mentioning

confidence: 99%

“…In particular, it has shown to have a beneficial role in the diagnosis and analysis of several pathologies related to blood pressure [5], myocardial infarction [6,7], brain damage [8], depression [9], cardiac arrhythmia [10], diabetes [11] and renal failure [12]. HRV analysis has also shown correlations with sleep [13], drugs [14] or alcohol [15] assumption and smoking [16].…”

Section: Introductionmentioning

confidence: 99%

Cyclostationary Analysis for Heart Rate Variability

Ambrosanio¹,

Baselice²

2018

Open Bioinforma. J.

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Background:During the last years, cyclostationarity has emerged as a new approach for the analysis of a certain type of non-stationary signals. This theoretical tool allows us to identify periodicity in signals which cannot be identified easily but also to separate useful signals for other interfering contributions that overlap in the spectral support. Objective:The aim of this work is the exploitation of cyclostationary theory to enhance standard methodologies for the study of heart rate variability. In this framework, a preliminary analysis on healthy patients is proposed to be extended further on pathological patients with the perspective to improve (hopefully) the diagnostic power of some cardiac dysfunctions due to the more complete set of information provided by this analysis. Methods:The proposed approach involves an initial band-pass filtering step in the range 0.5 -40 Hz of the recorded ECG signal, followed by a first-order derivative filter to reduce the effects of P and T waves and to emphasise the QRS contribution. After that, the autocorrelation function is evaluated and the Cyclic Power Spectrum (CPS) is computed. From this two-dimensional information, a onedimensional plot is derived via the evaluation of a folded-projected CPS to be compared with standard Lomb-Scargle spectrum. Results:The proposed analysis has been tested on both numerical simulations as well as for the processing of real data which are available online in the Physionet database. Conclusion:The proposed cyclostationary analysis has shown a good agreement with the results provided by the classical Lomb-Scargle spectrum in the processing of real data, underlining some contributions in the high-frequency bandwidth which are not visible by means of standard processing.

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“…In recent years, a great effort was put on the development of automatic classification of sleep stages using biological signals that can be used at home in response to the move for self-monitoring of health and the obvious advantages of regular sleep monitoring at affordable cost for sleep quality monitoring ( Scherz et al, 2017 ). Most new methods use ECG and particularly heart rate variability (HRV) as a key component ( Radha et al, 2019 ; Sun et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%