Background Multinight monitoring can be helpful for the diagnosis and management of obstructive sleep apnea (OSA). For this purpose, it is necessary to be able to detect OSA in real time in a noisy home environment. Sound-based OSA assessment holds great potential since it can be integrated with smartphones to provide full noncontact monitoring of OSA at home. Objective The purpose of this study is to develop a predictive model that can detect OSA in real time, even in a home environment where various noises exist. Methods This study included 1018 polysomnography (PSG) audio data sets, 297 smartphone audio data sets synced with PSG, and a home noise data set containing 22,500 noises to train the model to predict breathing events, such as apneas and hypopneas, based on breathing sounds that occur during sleep. The whole breathing sound of each night was divided into 30-second epochs and labeled as “apnea,” “hypopnea,” or “no-event,” and the home noises were used to make the model robust to a noisy home environment. The performance of the prediction model was assessed using epoch-by-epoch prediction accuracy and OSA severity classification based on the apnea-hypopnea index (AHI). Results Epoch-by-epoch OSA event detection showed an accuracy of 86% and a macro F1-score of 0.75 for the 3-class OSA event detection task. The model had an accuracy of 92% for “no-event,” 84% for “apnea,” and 51% for “hypopnea.” Most misclassifications were made for “hypopnea,” with 15% and 34% of “hypopnea” being wrongly predicted as “apnea” and “no-event,” respectively. The sensitivity and specificity of the OSA severity classification (AHI≥15) were 0.85 and 0.84, respectively. Conclusions Our study presents a real-time epoch-by-epoch OSA detector that works in a variety of noisy home environments. Based on this, additional research is needed to verify the usefulness of various multinight monitoring and real-time diagnostic technologies in the home environment.

Background: There have been many studies in using intrathecal morphine order to reduce pain after obstetrics and gynecological surgeries, abdominal surgeries and orthopedic surgeries. These studies have shown that intrathecal morphine is very effective for pain relief after surgery. However, intrathecal morphine also has side effects especially in obstetric and gynecological surgeries such as pruritus, postoperative nausea and vomiting, and delayed respiratory depression. Although postoperative analgesia with intrathecal morphine has been widely used in obstetrics and gynecological surgeries, orthopedic surgeries, there have been very few studies on postoperative pain relief with intrathecal morphine for colorectal surgery. Laparoscopic colorectal surgery requires multimodal analgesia, so using intrathecal morphine to reduce postoperative pain in this surgery is essential in clinical practice. Therefore, studying the effectiveness of intrathecal morphine in this surgery is necessary, so we conducted this study. Objectives: To assess the effectiveness of analgesic and side effects of intrathecal morphine after laparoscopic colorectal surgery. Materials and Methods: This was a descriptive, cross-sectional study, including 63 patients undergoing laparoscopic colorectal cancer surgery with intrathecal morphine before general anesthesia. The degree of analgesia was assessed based on VAS. The postoperative side effects observed were postoperative nausea and vomiting, respiratory depression, and pruritus. Results: The analgesic effect at rest and on slight movement was 95.2%, and 88.9% respectively with VAS ≤ 3. The side effects were postoperative nausea and vomiting (6.3%), and pruritus (1.6%), both postoperative nausea and vomiting and pruritus (3.2%). In conclusion, 300µg intrathecal morphine showed a safe and positive analgesic effect for laparoscopic colorectal cancer surgery.

BACKGROUND Multinight monitoring can be helpful for the diagnosis and management of obstructive sleep apnea (OSA). For this purpose, it is necessary to be able to detect OSA in real time in a noisy home environment. Sound-based OSA assessment holds great potential since it can be integrated with smartphones to provide full noncontact monitoring of OSA at home. OBJECTIVE The purpose of this study is to develop a predictive model that can detect OSA in real time, even in a home environment where various noises exist. METHODS This study included 1018 polysomnography (PSG) audio data sets, 297 smartphone audio data sets synced with PSG, and a home noise data set containing 22,500 noises to train the model to predict breathing events, such as apneas and hypopneas, based on breathing sounds that occur during sleep. The whole breathing sound of each night was divided into 30-second epochs and labeled as “apnea,” “hypopnea,” or “no-event,” and the home noises were used to make the model robust to a noisy home environment. The performance of the prediction model was assessed using epoch-by-epoch prediction accuracy and OSA severity classification based on the apnea-hypopnea index (AHI). RESULTS Epoch-by-epoch OSA event detection showed an accuracy of 86% and a macro F₁-score of 0.75 for the 3-class OSA event detection task. The model had an accuracy of 92% for “no-event,” 84% for “apnea,” and 51% for “hypopnea.” Most misclassifications were made for “hypopnea,” with 15% and 34% of “hypopnea” being wrongly predicted as “apnea” and “no-event,” respectively. The sensitivity and specificity of the OSA severity classification (AHI≥15) were 0.85 and 0.84, respectively. CONCLUSIONS Our study presents a real-time epoch-by-epoch OSA detector that works in a variety of noisy home environments. Based on this, additional research is needed to verify the usefulness of various multinight monitoring and real-time diagnostic technologies in the home environment.