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時間周波数領域でのてんかん脳波識別に関する研究 ‐平均二乗根に基づく特徴抽出に着目して‐
https://doi.org/10.18997/00007023
https://doi.org/10.18997/0000702367fbbff6-4264-4f99-bc2d-73443dc22d41
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
sei_k_323.pdf (3.2 MB)
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| アイテムタイプ | 学位論文 = Thesis or Dissertation(1) | |||||||
|---|---|---|---|---|---|---|---|---|
| 公開日 | 2019-02-15 | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_db06 | |||||||
| 資源タイプ | doctoral thesis | |||||||
| タイトル | ||||||||
| タイトル | Classification of Epileptic Seizure EEG Signals in Time Frequency Domain - Focusing on Root Mean Square based Feature Extraction | |||||||
| 言語 | en | |||||||
| タイトル | ||||||||
| タイトル | 時間周波数領域でのてんかん脳波識別に関する研究 ‐平均二乗根に基づく特徴抽出に着目して‐ | |||||||
| 言語 | ja | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| 著者 |
Mahapatra, Arindam Gajendra
× Mahapatra, Arindam Gajendra
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| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | Epilepsy affects over 50 million people on an average yearly world wide. Epileptic Seizure is a generalised term which has broad classification depending on the reasons behind its occurrence. Parvez et al. when applied feature instantaneous bandwidth B2AM and time averaged bandwidth B2FM for classification of interictal and ictal on Freiburg data base, the result dipped low to 77.90% for frontal lobe whereas it was 80.20% for temporal lobe compare to the 98.50% of classification accuracy achieved on Bonn dataset with same feature for classification of ictal against interictal. We found reasons behind such low results are, first Parvez et al. has used first IMF of EMD for feature computation which mostly noised induce. Secondly, they used same kernel parameters of SVM as Bajaj et al. which they must have optimised with different dataset. But the most important reason we found is that two signals s1 and s2 can have same instantaneous bandwidth. Therefore, the motivation of the dissertation is to address the drawback of feature instantaneous bandwidth by new feature with objective of achieving comparable classification accuracy. In this work, we have classified ictal from healthy nonseizure interictal successfully first by using RMS frequency and another feature from Hilbert marginal spectrum then with its parameters ratio. RMS frequency is the square root of sum of square bandwidth and square of center frequency. Its contributing parameters ratio is ratio of center frequency square to square bandwidth. We have also used dominant frequency and its parameters ratio for the same purpose. Dominant frequency have same physical relevance as RMS frequency but different by definition, i.e. square root of sum of square of instantaneous band- width and square of instantaneous frequency. Third feature that we have used is by exploiting the equivalence of RMS frequency and dominant frequency (DF) to define root mean instantaneous frequency square (RMIFS) as square root of sum of time averaged bandwidth square and center frequency square. These features are average measures which shows good discrimination power in classifying ictal from interictal using SVM. These features, fr and fd also have an advantage of overcoming the draw back of square bandwidth and instantaneous bandwidth. RMS frequency that we have used in this work is different from generic root mean square analysis. We have used an adaptive thresholding algorithm to address the issue of false positive. It was able to increase the specificity by average of 5.9% on average consequently increasing the accuracy. Then we have applied morphological component analysis (MCA) with the fractional contribution of dominant frequency and other rest of the features like band- width parameter’s contribution and RMIFS frequency and its parameters and their ratio. With the results from proposed features, we validated our claim to overcome the drawback of instantaneous bandwidth and square bandwidth. | |||||||
| 言語 | en | |||||||
| 目次 | ||||||||
| 内容記述タイプ | TableOfContents | |||||||
| 内容記述 | 1 Introduction||2 Empirical Mode Decomposition||3 Root Mean Square Frequency||4 Root Mean Instantaneous Frequency Square||5 Morphological Component Analysis||6 Conclusion | |||||||
| 備考 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | 九州工業大学博士学位論文 学位記番号：生工博甲第323号 学位授与年月日：平成30年6月28日 | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | EEG | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | EMD | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | MCA | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | RMS Frequency | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | RMIFS Frequency | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | SVM | |||||||
| アドバイザー | ||||||||
| 堀尾, 恵一 | ||||||||
| 学位授与番号 | ||||||||
| 学位授与番号 | 甲第323号 | |||||||
| 学位名 | ||||||||
| 学位名 | 博士（工学） | |||||||
| 学位授与年月日 | ||||||||
| 学位授与年月日 | 2018-06-28 | |||||||
| 学位授与機関 | ||||||||
| 学位授与機関識別子Scheme | kakenhi | |||||||
| 学位授与機関識別子 | 17104 | |||||||
| 学位授与機関名 | 九州工業大学 | |||||||
| 学位授与年度 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | 平成30年度 | |||||||
| 出版タイプ | ||||||||
| 出版タイプ | VoR | |||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||
| アクセス権 | ||||||||
| アクセス権 | open access | |||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||
| ID登録 | ||||||||
| ID登録 | 10.18997/00007023 | |||||||
| ID登録タイプ | JaLC | |||||||
