The　aim　of　this　work　is　to　design　a　reliable　algorithm　for　the　automatic　detection　of　heart　valve　disorders　(HVDs)　without　any　segmentation　of　the　heart　sound　signals.　Teager-Kaiser　energy　operator　(TKEO)　and　rational　dilation　wavelet　transform　(RDWT)　are　utilized　to　extract　representative　features　in　order　to　detect　abnormal　patterns　in　PCG　signals　with　the　employment　of　deep　learning　model.　First,　TKEO　is　used　to　extract　the　instantaneous　energy　of　the　source　that　generates　the　Phonocardiogram　(PCG)　signal　rather　than　the　energy　of　the　signal　itself.　Then,　RDWT　is　employed　to　decompose　the　instantaneous　energy　of　the　PCG　signal　into　different　sub-bands.　The　oscillatory　characteristics　of　PCG　has　been　retained　in　these　sub-bands,　which　are　served　as　discriminant　features.　Third,　these　features　are　fed　to　one-dimensional　(ID)　convolutional　neural　networks　(CNN)　for　classification.　Finally,　experiments　including　two　types　of　classification　named　binary　classification　and　multi-class　classification,　are　carried　out　on　a　well-known　and　publicly　available　PCG　database　to　verify　the　effectiveness　of　the　proposed　method.　The　overall　average　accuracy　for　binary　(5　cases),　four-class　and　five-class　classification　is　reported　to　be　100%,　99.00%,　99.75%,　99.75%,　99.60%,　98.87%　and　98.10%,　respectively.　The　proposed　method　has　obtained　superior　accuracy　in　comparison　to　most　of　the　state-of-the-art　approaches　using　the　same　database.　©　2022　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.