Deep learning via ECG and PPG signals for prediction of depth of anesthesia

模式识别(心理学) 人工神经网络 卷积神经网络 脑电图 信号(编程语言) 心率 支持向量机
作者
Meghna Roy Chowdhury,Ravichandra Madanu,Maysam F. Abbod,Shou-Zen Fan,Jiann-Shing Shieh
出处
期刊:Biomedical Signal Processing and Control [Elsevier]
卷期号:68: 102663- 被引量:2
标识
DOI:10.1016/j.bspc.2021.102663
摘要

Abstract During surgeries, the amount of used anesthetic depends on the physical conditions of the patient and is immensely critical. The conventionally used BIS Quantro machine which measures the Bispectral Index (BIS) level in order to help doctors administer anesthesia, is quite expensive. In this paper, an economic, accurate and state-of-the-art technique is presented to predict the depth of anesthesia (DoA) via advanced deep learning models using 512 Hz Electrocardiogram (ECG) and 128 Hz Photoplethysmography (PPG). The study is conducted based on signal collected from 50 patients acquired during surgery at National Taiwan University Hospital (NTUH). First, heatmaps of the ECG and PPG signals (individual and combined subplots) are generated using MATLAB by filtering 5 s windows to match the frequency of the BIS Quantro Machine which is 0.2 Hz. Then, various deep learning models comprising 5, 6, 8, 10 and 19 layered CNNs are trained using data of 40 patients and tested using the remaining 10 patients. The heatmap images of ECG and PPG are fed as inputs to the CNN models separately and using two input channels. The best accuracy achieved is 86 % which is attained using 10 layered CNN with Tensorflow backend, with combined ECG and PPG heatmaps as inputs. This study uses inexpensive signals, minimum data reconstruction, minimum memory and timing constrains to achieve a decent accuracy, and so it can be used by even small hospitals.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
飘逸踏歌完成签到 ,获得积分0
刚刚
充电宝应助落寞丹萱采纳,获得10
刚刚
yanshapo发布了新的文献求助10
1秒前
脑洞疼应助发疯的游子采纳,获得10
3秒前
888发布了新的文献求助20
3秒前
SYLH完成签到,获得积分0
4秒前
lrj完成签到,获得积分20
4秒前
海带完成签到,获得积分10
4秒前
天天快乐应助笨笨的晓夏采纳,获得10
5秒前
高大小懒猪完成签到,获得积分10
6秒前
9秒前
大气的火龙果完成签到 ,获得积分10
9秒前
Charles完成签到,获得积分10
9秒前
薛洁洁的小糖应助888采纳,获得50
9秒前
紫色水晶之恋完成签到 ,获得积分10
11秒前
yanshapo完成签到,获得积分10
14秒前
16秒前
山海发布了新的文献求助10
16秒前
大海的DOI完成签到,获得积分10
17秒前
17秒前
火星上白羊完成签到 ,获得积分10
22秒前
小蘑菇应助科研通管家采纳,获得10
22秒前
研友_VZG7GZ应助科研通管家采纳,获得10
22秒前
梁三柏应助科研通管家采纳,获得20
22秒前
田様应助科研通管家采纳,获得10
22秒前
22秒前
桐桐应助科研通管家采纳,获得10
22秒前
汉堡包应助科研通管家采纳,获得10
22秒前
赤木完成签到 ,获得积分10
23秒前
海带发布了新的文献求助10
24秒前
女神金完成签到,获得积分10
25秒前
27秒前
山海完成签到,获得积分10
28秒前
研友_VZG7GZ应助parpate采纳,获得10
30秒前
Frost完成签到,获得积分10
31秒前
escapeace发布了新的文献求助30
31秒前
31秒前
39秒前
39秒前
parpate发布了新的文献求助10
42秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Les Mantodea de Guyane Insecta, Polyneoptera 1000
Structural Load Modelling and Combination for Performance and Safety Evaluation 1000
Conference Record, IAS Annual Meeting 1977 820
England and the Discovery of America, 1481-1620 600
電気学会論文誌D(産業応用部門誌), 141 巻, 11 号 510
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3572296
求助须知:如何正确求助?哪些是违规求助? 3142501
关于积分的说明 9448015
捐赠科研通 2843973
什么是DOI,文献DOI怎么找? 1563103
邀请新用户注册赠送积分活动 731630
科研通“疑难数据库(出版商)”最低求助积分说明 718640