Singular Spectrum Analysis of Sleep EEG in Insomnia

Abstract

In the present study, the Singular Spectrum Analysis (SSA) is applied to sleep EEG segments collected from healthy volunteers and patients diagnosed by either psycho physiological insomnia or paradoxical insomnia. Then, the resulting singular spectra computed for both C3 and C4 recordings are assigned as the features to the Artificial Neural Network (ANN) architectures for EEG classification in diagnose. In tests, singular spectrum of particular sleep stages such as awake, REM, stage1 and stage2, are considered. Three clinical groups are successfully classified by using one hidden layer ANN architecture with respect to their singular spectra. The results show that the SSA can be applied to sleep EEG series to support the clinical findings in insomnia if ten trials are available for the specific sleep stages. In conclusion, the SSA can detect the oscillatory variations on sleep EEG. Therefore, different sleep stages meet different singular spectra. In addition, different healthy conditions generate different singular spectra for each sleep stage. In summary, the SSA can be proposed for EEG discrimination to support the clinical findings for psycho-psychological disorders.

Weighted Linear Recurrent Forecasting in Singular Spectrum Analysis

“…Use the first 2 3 rd observations to construct many different training sets, each one containing one more observation than the previous one. Apply the remaining 1 3 rd of observations to build corresponding test sets, each one containing one fewer observation than the previous one (see Figure 1).…”

“…As a result, there are continuous attempts at developing the underlying theory of SSA and improving its forecasting methods. Whilst the review of all applications of SSA are beyond the scope of this paper, those interested are referred to [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Nowadays, there is a bulk of research on SSA to develop its theory and applications and few such examples can be found in [16][17][18][19][20][21][22].…”

Automatic Grouping in Singular Spectrum Analysis

“…The remarkable features of SSA are that neither a parametric model nor stationarity-type conditions have to be assumed for the time series [1]. The SSA method is attracting considerable interest due to its widespread capabilities and it has many applications in a variety of fields such as medicine [2][3][4][5], biology and genetics [6,7], finance and economics [8][9][10][11][12][13][14][15][16], engineering [17][18][19][20][21][22][23], and other fields [24][25][26]. Whole and precise details on the theory and applications of SSA can be found in [1,[27][28][29].…”

Vector and recurrent singular spectrum analysis: which is better at forecasting?

“…The Singular Spectrum Analysis (SSA) technique is a nonparametric time series analysis and forecasting technique which is transforming into an increasingly popular method for noise reduction and forecasting. Whilst it is not the objective of this paper to review all applications of SSA, we cite few of the recent articles as evidence of the increasing popularity of SSA (see for example, [1][2][3][4][5][6][7][8][9][10][11][12][13][14]). In brief, the SSA technique seeks to decompose a time series to identify the trend, signal, harmonic components and noise, and thereafter reconstructs a new, filtered time series which can be used for forecasting future data points [15].…”