基于声音的早期进餐开始检测：使用隐马尔可夫模型（HMM）。

文献信息

中文摘要

目的： 进餐检测对于1型糖尿病（T1D）患者的自动胰岛素输送系统至关重要，因为进餐是餐后血糖快速波动的主要驱动因素。当前的闭环系统依赖手动进餐宣告，这往往不准确或被省略，降低了安全性和自主性。为解决这一问题，我们开发了一种利用身体生理声音序列的自动进餐检测器。
方法与步骤： 数据集包括来自十名参与者（年龄24-30岁）的50段进餐录音，使用喉部佩戴的麦克风采集。其中20段录音在安静条件下收集，30段包含刻意、真实的干扰。音频信号经过预处理、分割并转换为46个特征。采用线性判别分析（LDA）作为基准，同时应用连续隐马尔可夫模型（HMM）来捕捉时间依赖性并估计进餐开始概率。
结果： 使用80:20的训练-测试划分和留一受试者交叉验证（LOSO CV）评估性能。采用可解释人工智能（XAI）技术来解释模型行为并识别进餐事件的信息性特征。LDA达到90%的准确率、78%的敏感性和12.3秒的检测延迟，而HMM在准确率和敏感性上均达到91%，平均检测时间为11.7秒。两种模型在安静条件下表现良好，但对背景干扰敏感。
结论： 这些发现证明了从生理声音中检测进餐开始的可行性。XAI分析显示，HMM有效捕捉了与咀嚼和吞咽相关的序列转换，提供了超越非时间方法的可解释见解。因此，HMM凭借其对时间结构的有效建模，显示出作为闭环胰岛素治疗快速、情境感知进餐分类器的潜力。
临床影响： 这项初步研究使用创新的喉部佩戴麦克风，通过捕捉咀嚼和吞咽声音来检测早期进餐摄入，为连续血糖监测（CGM）等传统方法提供了替代方案。

英文摘要

OBJECTIVE: Meal detection is essential for automated insulin delivery systems in patients with Type 1 Diabetes (T1D), as meals are the primary drivers of rapid postprandial glucose excursions. Current closed-loop systems depend on manual meal announcements, which are often inaccurate or omitted, reducing safety and autonomy. To address this, we developed an automatic meal detector leveraging physiological sound sequences from the body.
METHODS AND PROCEDURES: The dataset comprised 50 meal recordings from ten participants (aged 24-30 years) using a throat-mounted microphone. Twenty recordings were collected in quiet conditions, while 30 included deliberate, realistic disturbances. Audio signals were preprocessed, segmented, and transformed into 46 features. Linear Discriminant Analysis (LDA) was implemented as a benchmark, while continuous Hidden Markov Model (HMM)s were applied to capture temporal dependencies and estimate meal onset probability.
RESULTS: Performance was evaluated using both an 80:20 train-test split and Leave-One-Subject-Out Cross-Validation (LOSO CV). Explainable Artificial Intelligence (XAI) techniques were employed to interpret model behavior and identify informative features of meal events. LDA achieved 90% accuracy, 78% sensitivity, and a detection delay of 12.3 s, while HMM reached 91% for both accuracy and sensitivity, with an average detection time of 11.7 s. Both models performed well under quiet conditions but were sensitive to background disturbances.
CONCLUSION: These findings demonstrate the feasibility of detecting meal onset from physiological sounds. XAI analysis revealed that HMM effectively captures sequential transitions associated with chewing and swallowing, offering interpretable insights beyond non-temporal methods. Thus, HMM shows potential as a rapid, context-aware meal classifier for closed-loop insulin therapy, thanks to its effective modeling of temporal structure.
CLINICAL IMPACT: This pilot study uses an innovative throat-mounted microphone to detect early meal intake by capturing chewing and swallowing sounds, offering an alternative to traditional measures such as continuous glucose monitoring (CGM).