Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Rừng Kép Giai Tầng Đa Quy Mô cho Nhận diện Sinh Trắc Điện Tâm Đồ
Tóm tắt
Nhận diện sinh trắc điện tâm đồ (ECG) đã nổi lên như một chủ đề nghiên cứu nóng trong thập kỷ qua. Mặc dù một số kết quả hứa hẹn đã được báo cáo, đặc biệt trong việc sử dụng học đại diện thưa (SRL) và mạng nơ-ron sâu, việc nhận dạng mạnh mẽ cho dữ liệu quy mô nhỏ vẫn là một thách thức. Để giải quyết vấn đề này, chúng tôi tích hợp SRL vào một mô hình chuỗi sâu, và đề xuất mô hình rừng kép giai tầng đa quy mô (MDCBF) cho nhận diện sinh trắc ECG. Chúng tôi thiết kế trình tạo đặc trưng dựa trên rừng kép bằng cách kết hợp độ thưa chuẩn L1 và đại diện hợp tác chuẩn L2 để xử lý hiệu quả tiếng ồn. Sau đó, chúng tôi đề xuất một khung mạng sâu chuỗi, bao gồm mã hóa tín hiệu đa quy mô và mã hóa chuỗi sâu. Trong phần đầu tiên, chúng tôi thiết kế một phép toán tích lũy trọng số thích ứng, có thể khai thác triệt để thông tin phân biệt của các đoạn có tiếng ồn thấp. Trong mã hóa chuỗi sâu, chúng tôi đề xuất mã hóa lớp mà không cần lan truyền ngược để khai thác thêm các đặc trưng phân biệt hơn. Các thí nghiệm rộng rãi đã được thực hiện trên bốn cơ sở dữ liệu ECG quy mô nhỏ, và kết quả cho thấy phương pháp đề xuất hoạt động cạnh tranh với các phương pháp hiện đại nhất.
Từ khóa
#Nhận diện sinh trắc #điện tâm đồ #học đại diện thưa #mạng nơ-ron sâu #mô hình rừng kép #mã hóa tín hiệu.Tài liệu tham khảo
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