エレクトロカーディオグラム信号におけるノイズ没収のための進化的最適化ベースの下降型適応フィルター
Shubham Yadav1, Suman Kumar Saha2, Rajib Kar3
1Department of Computer Science and Engineering, C. V. Raman Global University, Bhubaneswar, Odisha, India. shubham.ydv@cgu-odisha.ac.in.
Physical and engineering sciences in medicine
|September 1, 2025
まとめ
この研究では,心電図 (ECG) シグナルをクリアするための適応ノイズキャンセラー (ANC) を最適化するためのスワームインテリジェンスアルゴリズムを導入します. 提案された方法は,ノイズと人工物を減らすことで,ECG信号の質を大幅に改善します.
科学分野:
- 生物医学工学
- シグナル処理
- コンピューター・インテリジェンス
背景:
- 心電図 (ECG) 信号は心臓診断に不可欠ですが,記録中に騒音や人工物で苦しんでいます.
- 信号の劣化は心臓状態の評価の正確性を損なうので,効果的なノイズキャンセリング技術が必要になります.
研究 の 目的:
- ECG信号を消音するために,スワームインテリジェンスベースの最適に調節されたアダプティブノイズキャンセラー (ANC) を提案し,評価する.
- シーガイル最適化アルゴリズム (SOA),NLSHADE,HGSAによって最適化されたANCの性能を比較する.
主な方法:
- SOA,NLSHADE,HGSAを使用して最適化されたANCの開発.
- 公共のECGデータセット (ADB,QTDB) を使って様々なSNRで添加的ホワイトガウスノイズで破損した検証.
- パーセンテージ・ルーツ・ミーン・スクエア・デバイアーション (PRD),ミーン・スクエア・エラー (MSE),シグナル・トゥ・ノイズ・レート (SNR) の改善などのメトリックを用いたパフォーマンス評価.
主要な成果:
- 提案されたSOA支援のANCは,PRD3.40E-03とMSE1.35E-11で優れた性能を達成しました.
- 平均SNRの改善は10.986dBで,ベンチマークアルゴリズムを上回りました.
- ウィルコクソンサインランクテストを用いた統計的検証は,SOAによるANCの有効性を確認した.
結論:
- 群集インテリジェンス最適化,特にSOAは,ECG信号の質を高めるための強力なアプローチを提供します.
- 提案されたANCは人工物を効果的に除去し,心臓病の診断の精度を向上させます.
- この技術は,臨床応用のための生物医学信号処理の重要な進歩です.
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